WO2022161011A1 - 生成图像的方法和电子设备 - Google Patents

生成图像的方法和电子设备 Download PDF

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Publication number
WO2022161011A1
WO2022161011A1 PCT/CN2021/139250 CN2021139250W WO2022161011A1 WO 2022161011 A1 WO2022161011 A1 WO 2022161011A1 CN 2021139250 W CN2021139250 W CN 2021139250W WO 2022161011 A1 WO2022161011 A1 WO 2022161011A1
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Prior art keywords
image
electronic device
feature point
ith
registration
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PCT/CN2021/139250
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English (en)
French (fr)
Inventor
于頔
郭宏伟
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华为技术有限公司
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Publication of WO2022161011A1 publication Critical patent/WO2022161011A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/95—Computational photography systems, e.g. light-field imaging systems
    • H04N23/951—Computational photography systems, e.g. light-field imaging systems by using two or more images to influence resolution, frame rate or aspect ratio
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/60—Control of cameras or camera modules
    • H04N23/698—Control of cameras or camera modules for achieving an enlarged field of view, e.g. panoramic image capture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/80—Camera processing pipelines; Components thereof
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture

Definitions

  • the present application relates to the field of terminal artificial intelligence (artificial intelligence, AI), in particular to the field of image generation, and in particular to a method and electronic device for generating images.
  • AI artificial intelligence
  • Hyperspectral imaging technology is a rapidly developing branch technology in remote sensing technology. Through hyperspectral sensors mounted on different space platforms, in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum, tens to hundreds of continuous In addition, the subdivided spectral bands can simultaneously image the target area, which can not only obtain the spatial information of the target area, but also obtain the spectral information of the target area. Therefore, hyperspectral imaging technology has great application value and broad development prospects in the field of image generation.
  • the more popular hyperspectral sensors are single-point or line-array spectral sensors, which can scan targets to obtain single-point or line-array hyperspectral data, and then generate area-array hyperspectral images from the single-point or line-array hyperspectral data.
  • the traditional technology mostly adopts the push-broom imaging method, which scans the target at a fixed push-broom speed with the help of a complex mechanical device, and then processes the single-point or linear array hyperspectral data obtained by scanning based on the push-broom speed to obtain an area array. Hyperspectral image.
  • the present application provides a method and electronic device for generating an image, which does not require assistance from complex mechanical devices, has strong applicability, and greatly improves the ability of the electronic device to generate area array hyperspectral images.
  • the present application provides a method for generating an image, which is applied to an electronic device including an image acquisition device, the method comprising: acquiring n first images and n first spectral data collected by the image acquisition device; wherein one The first spectral data corresponds to a first image, the first spectral data is lattice hyperspectral data or linear array hyperspectral data, and n is a positive integer greater than or equal to 2; required when determining the i-th first image for image registration
  • the registration parameters of , i traverse 1 to n use the registration parameters to process the i-th first spectral data to obtain the i-th second spectral data; wherein, the i-th first spectral data corresponds to the i-th first spectral data.
  • a second image is generated, and the second image is an area array hyperspectral image.
  • the image acquisition device may include a visible light camera and a hyperspectral sensor, the first image may be a visible light image, and the second spectral data is obtained by transforming the first spectral data, such as coordinate transformation.
  • the electronic device uses the registration parameters required for the registration of visible light images to transform the lattice hyperspectral data or the linear hyperspectral data, and finally splices it into an area hyperspectral image.
  • This method does not require the assistance of complex mechanical equipment to generate area hyperspectral images, and has strong applicability, which greatly improves the ability of electronic devices to generate area hyperspectral images.
  • determining the registration parameters required for image registration of the i-th first image includes: selecting the j-th first image among the n first images As the reference image of the i-th first image, where the j-th first image is different from the i-th first image; perform feature extraction on the j-th first image to obtain the first feature points; Perform feature extraction on the image to obtain a second feature point, and determine a matching feature point pair according to the first feature point and the second feature point; and determine the registration parameter of the i-th first image according to the coordinate information of the matched feature point pair.
  • the electronic device performs feature extraction on the reference image and the i-th first image to obtain a matching feature point pair, and then determines the registration parameters of the i-th first image, which is the subsequent pairing of lattice hyperspectral data.
  • the linear array hyperspectral data can be transformed and processed to provide data preparation, which does not require the assistance of complex mechanical equipment, and has strong applicability.
  • the j-th first image is a previous frame image adjacent to the i-th first image
  • the method further includes: using the i-th first image , perform image registration on the i-th first image, and obtain the i-th first image after registration; take the i-th first image after registration as the i+1-th first image Baseline image.
  • the image adjacent to the i-th first image is selected as its reference image, and the i-th first image after image registration is continued as the reference image of the i+1-th first image.
  • the n first images are all registered to the same spatial domain, which further improves the accuracy of image registration, and provides more accurate data preparation for subsequent transformation processing of lattice hyperspectral data or linear hyperspectral data, thereby improving the results. Accuracy of area array hyperspectral images.
  • the image acquisition device includes a TOF lens, and determines a matching feature point pair according to the first feature point and the second feature point, which specifically includes: acquiring the first feature point of the first feature point. One three-dimensional coordinate information and the second three-dimensional coordinate information of the second feature point; wherein, the first three-dimensional coordinate information and the second three-dimensional coordinate information are calculated according to the collected data of the TOF lens; according to the first three-dimensional coordinate information and For the second three-dimensional coordinate information, the first feature point and the second feature point are matched to determine the matched feature point pair.
  • the electronic device uses the three-dimensional coordinate information of the feature point pairs to perform matching, which can improve the accuracy of the matched feature point pairs, thereby improving the accuracy of the registration parameters.
  • an inertial sensor is configured in the electronic device, and the feature extraction is performed on the jth first image to obtain the first feature point, and feature extraction is performed on the ith first image.
  • the method further includes: performing image optimization on the jth first image according to the sensor data collected by the inertial sensor when the image acquisition device collects the jth first image, to obtain the optimized jth first image.
  • the first image according to the sensor data collected by the inertial sensor when the image collecting device collects the ith first image, image optimization is performed on the ith first image to obtain the optimized ith first image.
  • the electronic device first performs image optimization on the first image and then performs the process of feature extraction, which can improve the accuracy of the obtained feature points, thereby improving the accuracy of the obtained registration parameters.
  • Transforming hyperspectral data or linear array hyperspectral data can improve the accuracy of the obtained area hyperspectral image.
  • the above method further includes: based on the second image, using a preset algorithm to dehaze the target image to obtain a dehazed target image; wherein the target image is The i-th first image, or the target image, is a stitched image generated by performing image registration on the n first images.
  • the haze image is dehazed, which can improve the clarity of the visible light image and further improve the image quality.
  • the present application provides an apparatus, the apparatus is included in an electronic device, and the apparatus has a function of implementing the behavior of the electronic device in the above-mentioned first aspect and possible implementation manners of the above-mentioned first aspect.
  • the functions can be implemented by hardware, or by executing corresponding software by hardware.
  • the hardware or software includes one or more modules or units corresponding to the above functions. For example, processing modules or units, etc.
  • the present application provides an electronic device, the electronic device includes: an image acquisition device, one or more processors, one or more memories, and a module in which multiple application programs are installed; the memory stores one or more programs , when one or more programs are executed by the processor, the electronic device is made to execute any one of the methods in the technical solutions of the first aspect.
  • the present application provides a chip including a processor.
  • the processor is adapted to read and execute the computer program stored in the memory to perform the method of the first aspect and any possible implementations thereof.
  • the chip further includes a memory, and the memory is connected to the processor through a circuit or a wire.
  • the chip further includes a communication interface.
  • the present application provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the processor is made to execute any one of the technical solutions of the first aspect method.
  • the present application provides a computer program product, the computer program product includes: computer program code, when the computer program code is run on an electronic device, the electronic device is made to perform any one of the methods in the technical solutions of the first aspect.
  • FIG. 1 is a schematic structural diagram of an example of an electronic device provided by an embodiment of the present application.
  • FIG. 2 is a block diagram of a software structure of an electronic device provided by an embodiment of the present application.
  • FIG. 3 is a schematic diagram of a system architecture of an example of a method for generating an image provided by an embodiment of the present application
  • FIG. 4 is a schematic flowchart of an example of a method for generating an image provided by an embodiment of the present application
  • FIG. 5 is a schematic diagram of an example of a photographing interface of an electronic device provided by an embodiment of the present application.
  • FIG. 6 is a schematic flowchart of another example of a method for generating an image provided by an embodiment of the present application.
  • FIG. 7 is a schematic flowchart of another example of a method for generating an image provided by an embodiment of the present application.
  • first”, “second” and “third” are only used for descriptive purposes, and should not be understood as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature defined as “first”, “second”, “third” may expressly or implicitly include one or more of that feature.
  • hyperspectral imaging technology can be used to detect the two-dimensional geometric spatial information and one-dimensional spectral information of the target object, and obtain continuous and narrow-band image data with high spectral resolution.
  • hyperspectral sensors due to the volume and cost of hyperspectral sensors, the most commonly used hyperspectral sensors on the market are single-point or line-array sensors.
  • an embodiment of the present application provides a method for generating an image, which can be applied to mobile phones, tablet computers, wearable devices, in-vehicle devices, augmented reality (AR)/virtual reality (VR) devices, Notebook computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (personal digital assistants, PDAs) and other electronic devices including image acquisition devices.
  • the above-mentioned image acquisition device may include a camera, a hyperspectral sensor, and the like.
  • the above electronic device uses the image registration information of the visible light image collected by the image collection device, combined with the lattice or line array hyperspectral data collected by the hyperspectral sensor, to generate an area array hyperspectral image.
  • the method for generating an image provided by the embodiment of the present application can generate an area array hyperspectral image without the aid of an external mechanical device, and has strong applicability. It should be clear that the embodiments of the present application do not impose any limitations on the specific types of electronic devices.
  • FIG. 1 is a schematic structural diagram of an example of an electronic device 100 provided by an embodiment of the present application.
  • the electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charge management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2 , mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber identification module (subscriber identification module, SIM) card interface 195 and so on.
  • SIM Subscriber identification module
  • the sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light. Sensor 180L, bone conduction sensor 180M, etc.
  • the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the electronic device 100 .
  • the electronic device 100 may include more or less components than shown, or combine some components, or separate some components, or arrange different components.
  • the illustrated components may be implemented in hardware, software, or a combination of software and hardware.
  • the processor 110 may include one or more processing units, for example, the processor 110 may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), controller, memory, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU) Wait. Wherein, different processing units may be independent devices, or may be integrated in one or more processors.
  • application processor application processor, AP
  • modem processor graphics processor
  • graphics processor graphics processor
  • ISP image signal processor
  • controller memory
  • video codec digital signal processor
  • DSP digital signal processor
  • NPU neural-network processing unit
  • the controller may be the nerve center and command center of the electronic device 100 .
  • the controller can generate an operation control signal according to the instruction operation code and timing signal, and complete the control of fetching and executing instructions.
  • a memory may also be provided in the processor 110 for storing instructions and data.
  • the memory in processor 110 is cache memory. This memory may hold instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to use the instruction or data again, it can be called directly from memory. Repeated accesses are avoided and the latency of the processor 110 is reduced, thereby increasing the efficiency of the system.
  • the processor 110 may include one or more interfaces.
  • the interface may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, a universal asynchronous transceiver (universal asynchronous transmitter) receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and / or universal serial bus (universal serial bus, USB) interface, etc.
  • I2C integrated circuit
  • I2S integrated circuit built-in audio
  • PCM pulse code modulation
  • PCM pulse code modulation
  • UART universal asynchronous transceiver
  • MIPI mobile industry processor interface
  • GPIO general-purpose input/output
  • SIM subscriber identity module
  • USB universal serial bus
  • the I2C interface is a bidirectional synchronous serial bus that includes a serial data line (SDA) and a serial clock line (SCL).
  • the processor 110 may contain multiple sets of I2C buses.
  • the I2S interface can be used for audio communication.
  • the processor 110 may contain multiple sets of I2S buses.
  • the processor 110 may be coupled with the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170 .
  • the PCM interface can also be used for audio communications, sampling, quantizing and encoding analog signals.
  • the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface.
  • the UART interface is a universal serial data bus used for asynchronous communication.
  • the bus may be a bidirectional communication bus. It converts the data to be transmitted between serial communication and parallel communication.
  • a UART interface is typically used to connect the processor 110 with the wireless communication module 160 .
  • the MIPI interface can be used to connect the processor 110 with peripheral devices such as the display screen 194 and the camera 193 .
  • MIPI interfaces include camera serial interface (CSI), display serial interface (DSI), etc.
  • the processor 110 communicates with the camera 193 through a CSI interface, so as to realize the photographing function of the electronic device 100 .
  • the processor 110 communicates with the display screen 194 through the DSI interface to implement the display function of the electronic device 100 .
  • the GPIO interface can be configured by software.
  • the GPIO interface can be configured as a control signal or as a data signal.
  • the GPIO interface may be used to connect the processor 110 with the camera 193, the display screen 194, the wireless communication module 160, the audio module 170, the sensor module 180, and the like.
  • the GPIO interface can also be configured as I2C interface, I2S interface, UART interface, MIPI interface, etc.
  • the USB interface 130 is an interface that conforms to the USB standard specification, and may specifically be a Mini USB interface, a Micro USB interface, a USB Type C interface, and the like.
  • the USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transmit data between the electronic device 100 and peripheral devices. It can also be used to connect headphones to play audio through the headphones.
  • the interface can also be used to connect other electronic devices, such as AR devices.
  • the interface connection relationship between the modules illustrated in the embodiments of the present application is only a schematic illustration, and does not constitute a structural limitation of the electronic device 100 .
  • the electronic device 100 may also adopt different interface connection manners in the foregoing embodiments, or a combination of multiple interface connection manners.
  • the charging management module 140 is used to receive charging input from the charger.
  • the charger may be a wireless charger or a wired charger.
  • the charging management module 140 may receive charging input from the wired charger through the USB interface 130 .
  • the charging management module 140 may receive wireless charging input through a wireless charging coil of the electronic device 100 . While the charging management module 140 charges the battery 142, the power management module 141 can also supply power to the electronic device.
  • the power management module 141 is used for connecting the battery 142 , the charging management module 140 and the processor 110 .
  • the power management module 141 receives input from the battery 142 and/or the charging management module 140 and supplies power to the processor 110 , the internal memory 121 , the external memory, the display screen 194 , the camera 193 , and the wireless communication module 160 .
  • the power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, battery health status (leakage, impedance).
  • the power management module 141 may also be provided in the processor 110 .
  • the power management module 141 and the charging management module 140 may also be provided in the same device.
  • the wireless communication function of the electronic device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modulation and demodulation processor, the baseband processor, and the like.
  • the mobile communication module 150 may provide wireless communication solutions including 2G/3G/4G/5G etc. applied on the electronic device 100 .
  • the mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA) and the like.
  • the mobile communication module 150 can receive electromagnetic waves from the antenna 1, filter and amplify the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation.
  • the mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and then turn it into an electromagnetic wave for radiation through the antenna 1 .
  • the modem processor may include a modulator and a demodulator.
  • the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal.
  • the demodulator is used to demodulate the received electromagnetic wave signal into a low frequency baseband signal. Then the demodulator transmits the demodulated low-frequency baseband signal to the baseband processor for processing.
  • the low frequency baseband signal is processed by the baseband processor and passed to the application processor.
  • the application processor outputs sound signals through audio devices (not limited to the speaker 170A, the receiver 170B, etc.), or displays images or videos through the display screen 194 .
  • the modem processor may be a stand-alone device.
  • the modem processor may be independent of the processor 110, and may be provided in the same device as the mobile communication module 150 or other functional modules.
  • the wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellites Wireless communication solutions such as global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared technology (IR).
  • WLAN wireless local area networks
  • BT Bluetooth
  • GNSS global navigation satellite system
  • FM frequency modulation
  • NFC near field communication
  • IR infrared technology
  • the wireless communication module 160 may be one or more devices integrating at least one communication processing module.
  • the wireless communication module 160 receives electromagnetic waves via the antenna 2 , frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110 .
  • the wireless communication module 160 can also receive the signal to be sent from the processor 110 , perform frequency modulation on it, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2 .
  • the electronic device 100 implements a display function through a GPU, a display screen 194, an application processor, and the like.
  • the GPU is a microprocessor for image processing, and is connected to the display screen 194 and the application processor.
  • the GPU is used to perform mathematical and geometric calculations for graphics rendering.
  • Processor 110 may include one or more GPUs that execute program instructions to generate or alter display information.
  • Display screen 194 is used to display images, videos, and the like.
  • Display screen 194 includes a display panel.
  • the display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (active-matrix organic light).
  • LED diode AMOLED
  • flexible light-emitting diode flexible light-emitting diode (flex light-emitting diode, FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (quantum dot light emitting diodes, QLED) and so on.
  • the electronic device 100 may include one or N display screens 194 , where N is a positive integer greater than one.
  • the electronic device 100 may implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, an application processor, and the like.
  • the ISP is used to process the data fed back by the camera 193 .
  • the shutter is opened, the light is transmitted to the camera photosensitive element through the lens, the light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to the ISP for processing, converting it into an image visible to the naked eye.
  • ISP can also perform algorithm optimization on image noise, brightness, and skin tone.
  • ISP can also optimize the exposure, color temperature and other parameters of the shooting scene.
  • the ISP may be provided in the camera 193 .
  • Camera 193 is used to capture still images or video.
  • the object is projected through the lens to generate an optical image onto the photosensitive element.
  • the photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor.
  • CMOS complementary metal-oxide-semiconductor
  • the photosensitive element converts the optical signal into an electrical signal, and then transmits the electrical signal to the ISP to convert it into a digital image signal.
  • the ISP outputs the digital image signal to the DSP for processing.
  • DSP converts digital image signals into standard RGB, YUV and other formats of image signals.
  • the electronic device 100 may include 1 or N cameras 193 , where N is a positive integer greater than 1.
  • the camera 193 may include a visible light camera for capturing visible light images.
  • the camera 193 may also include a time of flight (TOF) lens.
  • TOF is a measure of the time it takes for an object, particle or wave (eg, acoustic, electromagnetic, etc.) to travel a distance in a medium.
  • the TOF lens can transmit waves and receive the waves reflected by the above-mentioned objects after the above-mentioned waves encounter the object to be photographed.
  • the electronic device can obtain the time difference between the transmitted wave and the received reflected wave of the TOF lens, and/or the phase difference between the wave transmitted by the TOF lens and the received reflected wave.
  • the electronic device calculates the distance between the electronic device and the subject according to the above time difference and/or phase difference, and forms a set of distance depth data, thereby obtaining a 3D model or 3D image containing the depth information of the subject.
  • the above-mentioned waves may be infrared light, laser pulses, ultrasonic waves, or the like.
  • a digital signal processor is used to process digital signals, in addition to processing digital image signals, it can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy and so on.
  • Video codecs are used to compress or decompress digital video.
  • the electronic device 100 may support one or more video codecs.
  • the electronic device 100 can play or record videos of various encoding formats, such as: Moving Picture Experts Group (moving picture experts group, MPEG) 1, MPEG2, MPEG3, MPEG4 and so on.
  • MPEG Moving Picture Experts Group
  • MPEG2 moving picture experts group
  • MPEG3 MPEG4
  • MPEG4 Moving Picture Experts Group
  • the NPU is a neural-network (NN) computing processor.
  • NN neural-network
  • Applications such as intelligent cognition of the electronic device 100 can be implemented through the NPU, such as image recognition, face recognition, speech recognition, text understanding, and the like.
  • the external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100 .
  • the external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example to save files like music, video etc in external memory card.
  • Internal memory 121 may be used to store computer executable program code, which includes instructions.
  • the processor 110 executes various functional applications and data processing of the electronic device 100 by executing the instructions stored in the internal memory 121 .
  • the internal memory 121 may include a storage program area and a storage data area.
  • the storage program area can store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), and the like.
  • the storage data area may store data (such as audio data, phone book, etc.) created during the use of the electronic device 100 and the like.
  • the internal memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, universal flash storage (UFS), and the like.
  • the electronic device 100 may implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, an application processor, and the like. Such as music playback, recording, etc.
  • the pressure sensor 180A is used to sense pressure signals, and can convert the pressure signals into electrical signals.
  • the pressure sensor 180A may be provided on the display screen 194 .
  • the capacitive pressure sensor may be comprised of at least two parallel plates of conductive material. When a force is applied to the pressure sensor 180A, the capacitance between the electrodes changes.
  • the electronic device 100 determines the intensity of the pressure according to the change in capacitance. When a touch operation acts on the display screen 194, the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A.
  • the electronic device 100 may also calculate the touched position according to the detection signal of the pressure sensor 180A.
  • touch operations acting on the same touch position but with different touch operation intensities may correspond to different operation instructions. For example, when a touch operation whose intensity is less than the first pressure threshold acts on the short message application icon, the instruction for viewing the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, the instruction to create a new short message is executed.
  • the gyro sensor 180B may be used to determine the motion attitude of the electronic device 100 .
  • the angular velocity of electronic device 100 about three axes ie, x, y, and z axes
  • the gyro sensor 180B can be used for image stabilization.
  • the gyro sensor 180B detects the shaking angle of the electronic device 100, calculates the distance that the lens module needs to compensate according to the angle, and allows the lens to offset the shaking of the electronic device 100 through reverse motion to achieve anti-shake.
  • the gyro sensor 180B can also be used for navigation and somatosensory game scenarios.
  • the air pressure sensor 180C is used to measure air pressure.
  • the electronic device 100 calculates the altitude through the air pressure value measured by the air pressure sensor 180C to assist in positioning and navigation.
  • the magnetic sensor 180D includes a Hall sensor.
  • the electronic device 100 can detect the opening and closing of the flip holster using the magnetic sensor 180D.
  • the electronic device 100 can detect the opening and closing of the flip according to the magnetic sensor 180D. Further, according to the detected opening and closing state of the leather case or the opening and closing state of the flip cover, characteristics such as automatic unlocking of the flip cover are set.
  • the acceleration sensor 180E can detect the magnitude of the acceleration of the electronic device 100 in various directions (generally three axes).
  • the magnitude and direction of gravity can be detected when the electronic device 100 is stationary. It can also be used to identify the posture of electronic devices, and can be used in applications such as horizontal and vertical screen switching, pedometers, etc.
  • the electronic device 100 can measure the distance through infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 can use the distance sensor 180F to measure the distance to achieve fast focusing.
  • Proximity light sensor 180G may include, for example, light emitting diodes (LEDs) and light detectors, such as photodiodes.
  • the light emitting diodes may be infrared light emitting diodes.
  • the electronic device 100 emits infrared light to the outside through the light emitting diode.
  • Electronic device 100 uses photodiodes to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100 . When insufficient reflected light is detected, the electronic device 100 may determine that there is no object near the electronic device 100 .
  • the electronic device 100 can use the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear to talk, so as to automatically turn off the screen to save power.
  • Proximity light sensor 180G can also be used in holster mode, pocket mode automatically unlocks and locks the screen.
  • the ambient light sensor 180L is used to sense ambient light brightness.
  • the electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived ambient light brightness.
  • the ambient light sensor 180L can also be used to automatically adjust the white balance when taking pictures.
  • the ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in the pocket, so as to prevent accidental touch.
  • the fingerprint sensor 180H is used to collect fingerprints.
  • the electronic device 100 can use the collected fingerprint characteristics to realize fingerprint unlocking, accessing application locks, taking pictures with fingerprints, answering incoming calls with fingerprints, and the like.
  • the temperature sensor 180J is used to detect the temperature.
  • the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy.
  • Touch sensor 180K also called “touch panel”.
  • the touch sensor 180K may be disposed on the display screen 194 , and the touch sensor 180K and the display screen 194 form a touch screen, also called a “touch screen”.
  • the touch sensor 180K is used to detect a touch operation on or near it.
  • the touch sensor can pass the detected touch operation to the application processor to determine the type of touch event.
  • Visual output related to touch operations may be provided through display screen 194 .
  • the touch sensor 180K may also be disposed on the surface of the electronic device 100 , which is different from the location where the display screen 194 is located.
  • the bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire the vibration signal of the vibrating bone mass of the human voice. The bone conduction sensor 180M can also contact the pulse of the human body and receive the blood pressure beating signal. In some embodiments, the bone conduction sensor 180M can also be disposed in the earphone, combined with the bone conduction earphone.
  • the audio module 170 can analyze the voice signal based on the vibration signal of the voice part vibrating bone mass obtained by the bone conduction sensor 180M, so as to realize the voice function.
  • the application processor can analyze the heart rate information based on the blood pressure beat signal obtained by the bone conduction sensor 180M, and realize the function of heart rate detection.
  • the electronic device 100 further includes a hyperspectral sensor, and the hyperspectral sensor may be a dot-matrix sensor or a line-array sensor, the dot-matrix sensor may collect dot-matrix hyperspectral data, and the line-matrix sensor may collect line array hyperspectral data.
  • the hyperspectral sensor may be a dot-matrix sensor or a line-array sensor, the dot-matrix sensor may collect dot-matrix hyperspectral data, and the line-matrix sensor may collect line array hyperspectral data.
  • the keys 190 include a power-on key, a volume key, and the like. Keys 190 may be mechanical keys. It can also be a touch key.
  • the electronic device 100 may receive key inputs and generate key signal inputs related to user settings and function control of the electronic device 100 .
  • Motor 191 can generate vibrating cues.
  • the motor 191 can be used for vibrating alerts for incoming calls, and can also be used for touch vibration feedback.
  • touch operations acting on different applications can correspond to different vibration feedback effects.
  • the motor 191 can also correspond to different vibration feedback effects for touch operations on different areas of the display screen 194 .
  • Different application scenarios for example: time reminder, receiving information, alarm clock, games, etc.
  • the touch vibration feedback effect can also support customization.
  • the indicator 192 can be an indicator light, which can be used to indicate the charging state, the change of the power, and can also be used to indicate a message, a missed call, a notification, and the like.
  • the SIM card interface 195 is used to connect a SIM card.
  • the SIM card can be contacted and separated from the electronic device 100 by inserting into the SIM card interface 195 or pulling out from the SIM card interface 195 .
  • the electronic device 100 may support 1 or N SIM card interfaces, where N is a positive integer greater than 1.
  • the software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture.
  • the embodiments of the present application take an Android system with a layered architecture as an example to exemplarily describe the software structure of the electronic device 100 .
  • FIG. 2 is a block diagram of the software structure of the electronic device 100 according to the embodiment of the present application.
  • the layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Layers communicate with each other through software interfaces.
  • the Android system is divided into four layers, which are, from top to bottom, an application layer, an application framework layer, an Android runtime (Android runtime) and a system library, and a kernel layer.
  • the application layer can include a series of application packages.
  • the application package can include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message and so on.
  • the application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer.
  • the application framework layer includes some predefined functions.
  • the application framework layer may include window managers, content providers, view systems, telephony managers, resource managers, notification managers, and the like.
  • a window manager is used to manage window programs.
  • the window manager can get the size of the display screen, determine whether there is a status bar, lock the screen, take screenshots, etc.
  • Content providers are used to store and retrieve data and make these data accessible to applications.
  • Data can include videos, images, audio, calls made and received, browsing history and bookmarks, phone book, etc.
  • the view system includes visual controls, such as controls for displaying text, controls for displaying pictures, and so on. View systems can be used to build applications.
  • a display interface can consist of one or more views.
  • the display interface including the short message notification icon may include a view for displaying text and a view for displaying pictures.
  • the phone manager is used to provide the communication function of the electronic device 100 .
  • the management of call status including connecting, hanging up, etc.).
  • the resource manager provides various resources for the application, such as localization strings, icons, pictures, layout files, video files and so on.
  • the notification manager enables applications to display notification information in the status bar, which can be used to convey notification-type messages, and can disappear automatically after a brief pause without user interaction. For example, the notification manager is used to notify download completion, message reminders, etc.
  • the notification manager can also display notifications in the status bar at the top of the system in the form of graphs or scroll bar text, such as notifications of applications running in the background, and notifications on the screen in the form of dialog windows. For example, text information is prompted in the status bar, a prompt sound is issued, the electronic device vibrates, and the indicator light flashes.
  • the Android runtime includes core libraries and a virtual machine. Android runtime is responsible for scheduling and management of the Android system.
  • the core library consists of two parts: one is the function functions that the java language needs to call, and the other is the core library of Android.
  • the application layer and the application framework layer run in virtual machines.
  • the virtual machine executes the java files of the application layer and the application framework layer as binary files.
  • the virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, safety and exception management, and garbage collection.
  • a system library can include multiple functional modules. For example: surface manager (surface manager), media library (media library), 3D graphics processing library (eg: OpenGL ES), 2D graphics engine (eg: SGL), etc.
  • surface manager surface manager
  • media library media library
  • 3D graphics processing library eg: OpenGL ES
  • 2D graphics engine eg: SGL
  • the Surface Manager is used to manage the display subsystem and provides a fusion of 2D and 3D layers for multiple applications.
  • the media library supports playback and recording of a variety of commonly used audio and video formats, as well as still image files.
  • the media library can support a variety of audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.
  • the 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.
  • 2D graphics engine is a drawing engine for 2D drawing.
  • the kernel layer is the layer between hardware and software.
  • the kernel layer contains at least display drivers, camera drivers, audio drivers, and sensor drivers.
  • the method for generating an image provided by the embodiment of the present application can be applied to the system architecture shown in FIG. 3 .
  • the electronic device may collect multiple visible light images and multiple hyperspectral data, and the hyperspectral data may be lattice hyperspectral data or linear hyperspectral data.
  • the hyperspectral data may be lattice hyperspectral data or linear hyperspectral data.
  • the following takes linear array hyperspectral data as an example for description.
  • the electronic device After the electronic device acquires multiple visible light images, it can perform image registration on the multiple visible light images to obtain registration parameters during image registration. Taking two visible light images A and B as an example, as shown in FIG. 3 , the electronic device can first perform image preprocessing on image A and image B, and then extract feature points in image A and feature points in image B. Then, the electronic device matches the feature points in the image A with the feature points in the image B to obtain a feature point pair. Finally, the electronic device can calculate and obtain the registration parameters of the spatial coordinate transformation between the image A and the image B according to the feature point pair, as shown in FIG.
  • the image preprocessing includes, but is not limited to, the process of correcting the image based on camera internal parameters and distortion parameters.
  • the feature points of the image can be determined by extracting the features of the pixel points in the image, and the features of the pixel points can include point features, line features and surface features; the extraction algorithm of the point features can include, but is not limited to, scale-invariant feature transformation. feature transform, SIFT) algorithm, SURF (speeded up robust features) algorithm and ORB (oriented fast and rotated brief) algorithm, the extraction algorithm of line features may include but not limited to log algorithm and canny algorithm, and the extraction algorithm of surface features may include but Not limited to region segmentation algorithms.
  • Algorithms for feature point matching include, but are not limited to, Euclidean distance algorithm, k-nearest neighbor algorithm (KNN), Hamming distance algorithm and similarity measurement algorithm. Algorithms for solving the transformation matrix include, but are not limited to, random sample consensus (RANSAC) algorithm, RHO algorithm, least median (LMEDS) algorithm, and least squares algorithm.
  • the electronic device uses the registration parameter to perform coordinate transformation on the linear hyperspectral data, and performs splicing processing on the linear hyperspectral data after the coordinate transformation, so as to obtain a surface Array hyperspectral images to achieve the technical effect of generating area array hyperspectral images without external mechanical equipment.
  • the electronic device can also perform coordinate transformation on the coordinates of pixels in the visible light image by using the above-mentioned registration parameters, so as to obtain the visible light image after image registration, and stitch the visible light images after image registration.
  • the electronic device may also perform post-processing on the spliced visible light image based on the obtained area hyperspectral image, such as image dehazing (as shown in FIG. 3 ), image enhancement, and the like.
  • the above-mentioned process of dehazing the spliced visible light image based on the area array hyperspectral image may include processes such as fog density estimation, image partitioning, image fusion, and image filtering.
  • processes such as fog density estimation, image partitioning, image fusion, and image filtering.
  • the electronic device can also use the collected data of the TOF lens, the collected data of the hardware sensor, and the output interactive prompt information to improve the accuracy of the above-mentioned registration parameters, thereby improving the accuracy of the obtained area array hyperspectral image.
  • the electronic device can calculate the depth information of the subject; the data collected by the hardware sensor can include the acceleration of the electronic device.
  • FIG. 4 is a schematic flowchart of an example of a method for generating an image provided by an embodiment of the present application, and the method includes:
  • S101 Acquire n first images and n first spectral data collected by an image acquisition device; wherein one first spectral data corresponds to one first image, and the first spectral data is lattice hyperspectral data or linear hyperspectral data , where n is a positive integer greater than or equal to 2.
  • the image acquisition device includes the visible light camera and the hyperspectral sensor described in the above-mentioned embodiment of FIG. 1 .
  • the visible light camera can collect visible light images (ie, the first image)
  • the hyperspectral sensor can collect lattice hyperspectral data or line array hyperspectral data (ie, first spectral data).
  • the electronic device can simultaneously collect a visible light image and a first spectral data. Therefore, in the continuous shooting or panoramic shooting mode, the electronic device can acquire multiple (eg, n) visible light images and multiple first spectral data at different angles of the target area in the same scene.
  • the position of the target area in the visible light image can be fixed, and the focal length of the visible light camera and the focal length of the hyperspectral sensor can be kept consistent.
  • the process of camera imaging is usually the process of converting the world coordinate system to the pixel coordinate system, that is, the world coordinate system (3d) - the camera coordinate system (3d) - the image plane coordinate system (2d) - the pixel coordinate system (2d). After such a level-by-level conversion, the coordinates of the target area in space are converted into pixel coordinates in the image.
  • the electronic device may further perform correction preprocessing on the visible light image based on the camera internal parameters and distortion parameters.
  • some prompt information may be displayed on the display screen of the electronic device.
  • the above-mentioned prompt information may be a horizontal center line displayed on the shooting interface, and the center line is used to assist the user to move the target area based on the center line as much as possible during shooting.
  • the above-mentioned prompt information may also include a rectangular frame, and the captured image frame may be displayed in the rectangular frame; before the electronic device starts to shoot, the above-mentioned rectangular frame may be located on one side, center or near the center area of the display interface; As the electronic device captures more and more image frames, the sides of the rectangular frame in the direction of the center line may gradually become longer, or the rectangular frame may gradually move along the center line. Therefore, the above prompt information can be used to control the deviation range between the visible light images, and obtain n visible light images with higher quality.
  • the above-mentioned prompt information can also be a vertical center line displayed on the shooting interface of the electronic device, or can also be a horizontal center line and a vertical center line displayed on the shooting interface of the electronic device at the same time, Further improve the shooting quality of visible light images.
  • S102 Determine the registration parameters required for image registration of the i-th first image, and i traverse 1 to n.
  • the electronic device may determine registration parameters required for image registration of each visible light image.
  • the registration parameters may be transformation matrices. Based on the transformation matrix, coordinate transformation can be performed on the coordinates of pixel points in the visible light image to complete image registration; in this embodiment, the first spectral data can also be coordinate transformed based on the transformation matrix to obtain second spectral data.
  • the ith visible light image may be used as a reference image for image registration, and the registration parameters corresponding to the ith visible light image are determined. For example, using the first visible light image as the reference image, the second visible light image is registered to the spatial domain of the first visible light image; using the second visible light image as the reference image, the third visible light image is registered to In the spatial domain of the second visible light image, and so on, the registration parameters required for the registration of each visible light image are obtained.
  • any one of the n visible light images may be used as the reference image of the remaining n-1 images, and the registration parameters corresponding to the remaining n-1 visible light images are determined.
  • the first visible light image is selected as the reference image, and the second to nth visible light images are registered to the spatial domain of the first visible light image.
  • the electronic device can use the registration parameters corresponding to the visible light image to process the first spectral data, that is, Using the registration parameters of the ith visible light image, the first spectral data corresponding to the ith visible light image is processed to obtain second spectral data.
  • the registration parameters of the ith visible light image can be used to perform coordinate transformation on the first spectral data corresponding to the ith visible light image, so that the first spectral data are all located in the same reference coordinate system, obtaining Second spectral data.
  • the above-mentioned corresponding second spectral data is obtained for each first spectral data, then there are a total of n second spectral data, and these n second spectral data are still lattice hyperspectral data or linear hyperspectral data , then the electronic device can stitch the n second spectral data to generate an area array hyperspectral image.
  • the electronic device uses the registration parameters required for the registration of visible light images to transform the dot matrix hyperspectral data or the linear matrix hyperspectral data, and finally splices it into an area hyperspectral image.
  • the method can generate area array hyperspectral images without the assistance of complex mechanical equipment, and has strong applicability, which greatly improves the ability of electronic devices to generate area array hyperspectral images.
  • the process of determining the registration parameters required for image registration for the i-th first image in the above S102 may include:
  • S201 Select the j-th first image among the n first images as the reference image of the i-th first image, where the j-th first image is different from the i-th first image.
  • the ith visible light image or any other visible light image is selected as the ith visible light image.
  • the base image for the image may also be the previous frame image adjacent to the ith visible light image, that is, j may be i-1, then, the electronic device is using the registration parameter of the ith first image to pair. Image registration is performed on the i-th first image, and after the i-th first image after registration is obtained, the registered i-th first image can be used as a reference image of the i+1-th first image.
  • the third visible light image is registered as the reference image.
  • Register the visible light image to its spatial domain and then use the registered third visible light image as the reference image, register the fourth visible light image to its spatial domain, and so on, so that n
  • the visible light images are registered to the same spatial domain, which improves the image registration accuracy of each visible light image.
  • S203 Determine the registration parameter of the i-th first image according to the coordinate information of the matched pair of feature points.
  • the electronic device can perform feature extraction on the two visible light images respectively to determine the matching feature point pair, and according to the feature point pair The coordinate information of , determines the registration parameters of the ith visible light image.
  • the process of determining the registration parameters is described by taking the first visible light image A (reference image) and the second visible light image B for image registration as an example: First, extract the features and The features of each pixel point in the image B are used to determine the first feature point in the image A and the second feature point in the image B.
  • the pixel point features may include point features, line features, and surface features.
  • feature point matching is performed based on the first feature point of the image A and the second feature point of the image B, and the matched feature point pair is determined. Finally, based on the coordinate information of the matched feature point pairs, the transformation matrix between image A and image B is determined.
  • (a, b) is a matching feature point pair, where a is a feature point of image A, and b is a feature point of image B.
  • the coordinates of a are (x 1 , y 1 )
  • the coordinates of b are The coordinates are (x 2 , y 2 )
  • H is the transformation matrix used when image B is registered to image A , the registration parameters.
  • the electronic device performs feature extraction on the reference image and the i-th visible light image to obtain a matching feature point pair, and then determines the registration parameter of the i-th visible light image, which is the subsequent pairing of the dot matrix hyperspectral data or line.
  • the array hyperspectral data is transformed and processed to provide data preparation, which does not require the assistance of complex mechanical equipment, and has strong applicability.
  • the above image acquisition device further includes the TOF lens described in the above embodiment of FIG.
  • the three-dimensional coordinate information of each pixel in the visible light image corresponds to the electronic device.
  • the electronic device can obtain the three-dimensional coordinate information of the first feature point and the coordinate information of the second feature point.
  • the process of determining the matched pair of feature points may include: according to the three-dimensional coordinate information of the first feature point and the three-dimensional coordinate information of the second feature point, using the Euclidean distance algorithm, KNN algorithm, Hamming distance algorithm or similar in the above embodiment Algorithms such as a sex measurement algorithm match the first feature point and the second feature point, and determine the matched feature point pair, so as to improve the accuracy of the matched feature point pair, thereby improving the accuracy of the registration parameters.
  • Algorithms such as a sex measurement algorithm
  • the captured visible light image may also be recorded first.
  • an inertial sensor such as sensors such as an accelerometer and a gyroscope, can be configured in the electronic device, and the inertial sensor can collect sensor data representing the motion state of the electronic device in real time.
  • the image acquisition device collects the i-th visible light image
  • the inertial sensor will also collect the sensor data at the corresponding time.
  • the inertial sensor When collecting the jth visible light image, the inertial sensor will also collect the sensor data at the corresponding time; then, the electronic device can collect the jth visible light image according to the image acquisition device.
  • the optimized jth visible light image is obtained; according to the sensor data collected by the inertial sensor when the ith visible light image is collected by the image acquisition device Image optimization is performed on each visible light image, and the optimized ith visible light image is obtained.
  • the inertial sensor can collect the triaxial angular velocity and triaxial acceleration of the electronic device.
  • the electronic device can use the Kalman filter method to filter the three-axis angular velocity and the three-axis acceleration, use the Euler dynamics equation to convert the three-axis angular velocity into Euler angles, and use the uniform acceleration motion formula to convert the three-axis acceleration into three-axis displacement, Thereby the rotation matrix and translation matrix are obtained.
  • the electronic device can calculate the background optical flow of the i-th visible light image according to the rotation matrix and the translation matrix, and use the RANSAC algorithm to perform robust estimation of the background optical flow of the i-th visible light image to convert the background optical flow into the background optical flow.
  • the existing moving optical flow is removed, and the image optimization of the i-th visible light image is completed.
  • the process of performing image optimization on the jth visible light image by the electronic device is similar to the process of performing image optimization on the ith visible light image, and details are not described herein again.
  • the electronic device first performs image optimization on the visible light image and then performs the process of feature extraction, which can improve the accuracy of the feature points of the obtained image, thereby improving the accuracy of the matched feature point pairs, and then improving the obtained matching feature points.
  • transform the lattice hyperspectral data or the linear hyperspectral data to improve the accuracy of the obtained area hyperspectral image.
  • the electronic device may also perform dehazing or image enhancement processing on the visible light image based on the obtained second image, that is, the area array hyperspectral image, so as to improve the clarity of the visible light image and further improve the image quality of the visible light image.
  • the electronic device may use a preset algorithm to perform dehazing processing on the ith visible light image based on the second image to obtain the ith visible light image after defogging.
  • the above-mentioned preset algorithm may be a visible light and near-infrared image fusion algorithm
  • the electronic device may extract the brightness value of the corresponding pixel from the blue light channel and the second image of the ith visible light image, respectively, according to the brightness value of the corresponding pixel. Then, according to the fog density distribution, the brightness values of the corresponding pixels in the second image and the i-th visible light image are fused respectively to obtain the dehazing brightness value, and finally according to each de-hazing brightness value and the i-th The corresponding color values in the visible light images are generated, and the i-th visible light image after dehazing is generated.
  • the electronic device may continue to perform image filtering on the i-th visible light image to further improve the image quality of the visible light image.
  • the electronic device may also use a preset algorithm to perform dehazing processing on the target image based on the second image to obtain a dehazed target image, where the target image is obtained by performing dehazing on n visible light images.
  • the stitched image generated after image registration.
  • the electronic device determines the registration parameters required for image registration for the n visible light images, it can perform coordinate transformation on the pixel coordinates of the ith visible light image based on the registration parameters of the ith visible light image, to obtain After the registration of the ith visible light image, image stitching is performed on the n visible light images after image registration to generate a stitched visible light image; for example, a plurality of partial visible light images are stitched into a panoramic visible light image.
  • the visible light and near-infrared image fusion algorithm in the above-mentioned embodiment is used to fuse the spliced visible light image and the second image to obtain a dehazed spliced visible light image.
  • the electronic device may also extract the pixel values of the feature points in the visible light image and the pixel values of the corresponding points in the second image, and then, according to the pixel values of the corresponding points in the second image, analyze the visible light image
  • the pixel values of the feature points in the visible light image are fused to enhance the feature area in the visible light image and enhance the feature recognition effect of the visible light image.
  • defogging the visible light image containing haze can improve the clarity of the visible light image and thus improve the image quality.
  • the electronic device includes corresponding hardware and/or software modules for executing each function.
  • the present application can be implemented in hardware or a combination of hardware and computer software with the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functionality for each particular application in conjunction with the embodiments, but such implementations should not be considered beyond the scope of this application.
  • the electronic device can be divided into functional modules according to the above method examples.
  • each function can be divided into each functional module, such as a detection unit, a processing unit, a display unit, etc., or two or more
  • the functions are integrated in one module.
  • the above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. It should be noted that, the division of modules in the embodiments of the present application is schematic, and is only a logical function division, and there may be other division manners in actual implementation.
  • the electronic device provided in this embodiment is used to execute the above method for generating an image, so the same effect as the above implementation method can be achieved.
  • the electronic device may also include a processing module, a storage module and a communication module.
  • the processing module may be used to control and manage the actions of the electronic device.
  • the storage module may be used to support the electronic device to execute stored program codes and data, and the like.
  • the communication module can be used to support the communication between the electronic device and other devices.
  • the processing module may be a processor or a controller. It may implement or execute the various exemplary logical blocks, modules and circuits described in connection with this disclosure.
  • the processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, and the like.
  • the storage module may be a memory.
  • the communication module may specifically be a device that interacts with other electronic devices, such as a radio frequency circuit, a Bluetooth chip, and a Wi-Fi chip.
  • the electronic device involved in this embodiment may be a device having the structure shown in FIG. 1 .
  • the embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the processor is made to execute the method for generating an image in any of the foregoing embodiments.
  • Embodiments of the present application further provide a computer program product, which, when the computer program product runs on a computer, causes the computer to execute the above-mentioned relevant steps, so as to realize the method for generating an image in the above-mentioned embodiment.
  • the embodiments of the present application also provide an apparatus, which may specifically be a chip, a component or a module, and the apparatus may include a connected processor and a memory; wherein, the memory is used for storing computer execution instructions, and when the apparatus is running, The processor can execute the computer-executed instructions stored in the memory, so that the chip executes the method for generating an image in each of the foregoing method embodiments.
  • the electronic device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, for the beneficial effects that can be achieved, reference may be made to the above-provided method. The beneficial effects in the corresponding method will not be repeated here.
  • the disclosed apparatus and method may be implemented in other manners.
  • the apparatus embodiments described above are only illustrative.
  • the division of modules or units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components may be combined or May be integrated into another device, or some features may be omitted, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.
  • Units described as separate components may or may not be physically separated, and components shown as units may be one physical unit or multiple physical units, that is, may be located in one place, or may be distributed in multiple different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium.
  • a readable storage medium including several instructions to make a device (which may be a single chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods in the various embodiments of the present application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read only memory (ROM), random access memory (random access memory, RAM), magnetic disk or optical disk and other media that can store program codes.

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Abstract

本申请涉及终端AI领域，特别是图像生成领域，提供了一种生成图像的方法和电子设备，该方法包括：获取图像采集装置采集的n个第一图像和n个第一光谱数据；其中，一个第一光谱数据对应一个第一图像，第一光谱数据为点阵高光谱数据或者线阵高光谱数据，n为大于或者等于2的正整数；确定第i个第一图像确定进行图像配准时所需的配准参数，i遍历1至n；利用该配准参数对第i个第一光谱数据进行处理，得到第i个第二光谱数据，第i个第一光谱数据为对应于第i个第一图像的光谱数据，i遍历1至n；根据得到的n个第二光谱数据，生成第二图像，第二图像为面阵高光谱图像。该方法无需借助复杂的机械设备进行辅助生成面阵高光谱图像，适用性较强。

Description

生成图像的方法和电子设备
本申请要求于2021年01月29日提交国家知识产权局、申请号为202110129831.9、申请名称为“生成图像的方法和电子设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及终端人工智能(artificial intelligence，AI)领域，特别是图像生成领域，具体涉及一种生成图像的方法和电子设备。
背景技术
高光谱成像技术是遥感技术中快速发展的一个分支技术，通过搭载在不同空间平台上的高光谱传感器，在电磁波谱的紫外、可见光、近红外和中红外区域，以数十至数百个连续且细分的光谱波段对目标区域同时成像，不仅能够获得目标区域的空间信息，而且能够获得目标区域的光谱信息，因而高光谱成像技术在图像生成领域具有巨大的应用价值和广阔的发展前景。
目前较流行的高光谱传感器为单点式或线阵式光谱传感器，可以对目标进行扫描得到单点或线阵高光谱数据，再通过单点或线阵高光谱数据生成面阵高光谱图像。传统技术多采用推扫型成像的方法，借助复杂的机械装置以固定的推扫速度对目标进行扫描，然后基于推扫速度对扫描得到的单点或线阵高光谱数据进行处理，得到面阵高光谱图像。
但是，对于移动终端类的电子设备来说，难以内置上述复杂的机械装置，因此传统技术中推扫型成像方法的适用性较低。
发明内容
本申请提供了一种生成图像的方法和电子设备，无需借助复杂的机械设备进行辅助，适用性较强，大大提升了电子设备生成面阵高光谱图像的能力。
第一方面，本申请提供一种生成图像的方法，应用于包括图像采集装置的电子设备，该方法包括：获取图像采集装置采集的n个第一图像和n个第一光谱数据；其中，一个第一光谱数据对应一个第一图像，第一光谱数据为点阵高光谱数据或者线阵高光谱数据，n为大于或者等于2的正整数；确定第i个第一图像进行图像配准时所需的配准参数，i遍历1至n；利用配准参数对第i个第一光谱数据进行处理，得到第i个第二光谱数据；其中，第i个第一光谱数据为对应于第i个第一图像的光谱数据，i遍历1至n；根据得到的n个第二光谱数据，生成第二图像，第二图像为面阵高光谱图像。
其中，图像采集装置可以包括可见光摄像头和高光谱传感器，第一图像可以为可见光图像，第二光谱数据为对第一光谱数据进行变换处理所得到的，如坐标变换等。
上述实现方式中，电子设备采用可见光图像配准时所需的配准参数，对点阵高光谱数据或者线阵高光谱数据进行变换处理，最终拼接为面阵高光谱图像。该方法无需借助复杂 的机械设备进行辅助生成面阵高光谱图像，适用性较强，大大提升了电子设备生成面阵高光谱图像的能力。
结合第一方面，在第一方面的有些实现方式中，确定第i个第一图像进行图像配准时所需的配准参数，具体包括：选取n个第一图像中的第j个第一图像作为第i个第一图像的基准图像，其中，第j个第一图像不同于第i个第一图像；对第j个第一图像进行特征提取得到第一特征点、对第i个第一图像进行特征提取得到第二特征点，并根据第一特征点与第二特征点确定匹配的特征点对；根据匹配的特征点对的坐标信息，确定第i个第一图像的配准参数。
上述实现方式中，电子设备通过对基准图像和第i个第一图像进行特征提取以得到匹配的特征点对，进而确定第i个第一图像的配准参数，为后续对点阵高光谱数据或者线阵高光谱数据进行变换处理提供数据准备，进而无需借助复杂的机械设备进行辅助，适用性较强。
结合第一方面，在第一方面的有些实现方式中，上述第j个第一图像为与第i个第一图像相邻的前一帧图像，上述方法还包括：利用第i个第一图像的配准参数，对第i个第一图像进行图像配准，得到配准后的第i个第一图像；将配准后的第i个第一图像作为第i+1个第一图像的基准图像。
上述实现方式中，选取与第i个第一图像相邻的图像作为其基准图像，且进行图像配准后的第i个第一图像继续作为第i+1个第一图像的基准图像，可使得n个第一图像都配准至同一空间域上，进一步提高图像配准的精度，为后续对点阵高光谱数据或者线阵高光谱数据进行变换处理提供更精确的数据准备，进而提高得到的面阵高光谱图像的精度。
结合第一方面，在第一方面的有些实现方式中，上述图像采集装置包括TOF镜头，根据第一特征点与第二特征点确定匹配的特征点对，具体包括：获取第一特征点的第一三维坐标信息和第二特征点的第二三维坐标信息；其中，第一三维坐标信息和第二三维坐标信息均为根据TOF镜头的采集数据进行计算所得到的；根据第一三维坐标信息和第二三维坐标信息，对第一特征点和第二特征点进行匹配，确定匹配的特征点对。
上述实现方式中，电子设备采用特征点对的三维坐标信息进行匹配，可提高所匹配的特征点对的精度，进而提高配准参数的精度。
结合第一方面，在第一方面的有些实现方式中，电子设备中配置有惯性传感器，在对第j个第一图像进行特征提取得到第一特征点、对第i个第一图像进行特征提取得到第二特征点之前，上述方法还包括：根据图像采集装置采集第j个第一图像时、惯性传感器采集的传感器数据，对第j个第一图像进行图像优化，得到优化后的第j个第一图像；根据图像采集装置采集第i个第一图像时、惯性传感器采集的传感器数据，对第i个第一图像进行图像优化，得到优化后的第i个第一图像。
上述实现方式中，电子设备先对第一图像进行图像优化后再执行特征提取的过程，可提高得到的特征点的准确性，进而提高得到的配准参数的精度，在此基础上对点阵高光谱数据或者线阵高光谱数据进行变换处理，可提高得到的面阵高光谱图像的精度。
结合第一方面，在第一方面的有些实现方式中，上述方法还包括：基于第二图像，采用预设算法对目标图像进行去雾处理，得到去雾后的目标图像；其中，目标图像为第i个第一图像，或者目标图像为对n个第一图像进行图像配准后生成的拼接图像。
上述实现方式中，在面阵高光谱图像的基础上，基于高光谱图像与可见光图像融合方法，对含雾图像进行去雾处理，可以提高可见光图像的清晰度，进而提高图像质量。
第二方面，本申请提供一种装置，该装置包含在电子设备中，该装置具有实现上述第一方面及上述第一方面的可能实现方式中电子设备行为的功能。功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。硬件或软件包括一个或多个与上述功能相对应的模块或单元。例如，处理模块或单元等。
第三方面，本申请提供一种电子设备，电子设备包括：图像采集装置、一个或多个处理器、一个或多个存储器、安装有多个应用程序的模块；存储器存储有一个或多个程序，当一个或者多个程序被处理器执行时，使得电子设备执行第一方面的技术方案中任意一种方法。
第四方面，本申请提供一种芯片，包括处理器。处理器用于读取并执行存储器中存储的计算机程序，以执行第一方面及其任意可能的实现方式中的方法。
可选地，芯片还包括存储器，存储器与处理器通过电路或电线连接。
进一步可选地，芯片还包括通信接口。
第五方面，本申请提供一种计算机可读存储介质，计算机可读存储介质中存储了计算机程序，当计算机程序被处理器执行时，使得该处理器执行第一方面的技术方案中任意一种方法。
第六方面，本申请提供一种计算机程序产品，计算机程序产品包括：计算机程序代码，当计算机程序代码在电子设备上运行时，使得该电子设备执行第一方面的技术方案中任意一种方法。
附图说明
图1是本申请实施例提供的一例电子设备的结构示意图；
图2是本申请实施例提供的电子设备的软件结构框图；
图3是本申请实施例提供的一例生成图像的方法的系统架构示意图；
图4是本申请实施例提供的一例生成图像的方法的流程示意图；
图5是本申请实施例提供的一例电子设备拍摄界面的示意图；
图6是本申请实施例提供的另一例生成图像的方法的流程示意图；
图7是本申请实施例提供的又一例生成图像的方法的流程示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行描述。其中，在本申请实施例的描述中，除非另有说明，“/”表示或的意思，例如，A/B可以表示A或B；本文中的“和/或”仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，在本申请实施例的描述中，“多个”是指两个或多于两个。
以下，术语“第一”、“第二”、“第三”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”、“第三”的特征可以明示或者隐含地包括一个或者更多个该特征。
通常，为了更深入的分析一些目标物体的空间信息，可以借助高光谱成像技术探测目 标物体的二维几何空间信息及一维光谱信息，获取高光谱分辨率的连续、窄波段的图像数据。当前受制于高光谱传感器体积及成本的影响，市面上较多使用的高光谱传感器为单点式或线阵式传感器，而基于单点式或线阵式高光谱数据生成面阵高光谱图像时，需要结合外部机械设备，以固定的推扫速度对目标物体进行扫描，然后基于推扫速度对单点式或线阵式高光谱数据进行转换拼接，生成面阵高光谱图像。然而，对于手持式设备或移动设备，其难以内置大尺寸高成本低便携度的外部机械设备，则上述方法对手持式设备或移动设备并不适用。
有鉴于此，本申请实施例提供一种生成图像的方法，可以应用于手机、平板电脑、可穿戴设备、车载设备、增强现实(augmented reality，AR)/虚拟现实(virtual reality，VR)设备、笔记本电脑、超级移动个人计算机(ultra-mobile personal computer，UMPC)、上网本、个人数字助理(personal digital assistant，PDA)等包括图像采集装置的电子设备上。上述图像采集装置可以包括摄像头和高光谱传感器等。上述电子设备利用图像采集装置采集的可见光图像的图像配准信息，结合高光谱传感器采集的点阵或线阵高光谱数据，生成面阵高光谱图像。本申请实施例提供的生成图像的方法无需借助外部机械设备就可生成面阵高光谱图像，具有较强的适用性。应当明确，本申请实施例对电子设备的具体类型不作任何限制。
示例性的，图1是本申请实施例提供的一例电子设备100的结构示意图。电子设备100可以包括处理器110，外部存储器接口120，内部存储器121，通用串行总线(universal serial bus，USB)接口130，充电管理模块140，电源管理模块141，电池142，天线1，天线2，移动通信模块150，无线通信模块160，音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，传感器模块180，按键190，马达191，指示器192，摄像头193，显示屏194，以及用户标识模块(subscriber identification module，SIM)卡接口195等。其中传感器模块180可以包括压力传感器180A，陀螺仪传感器180B，气压传感器180C，磁传感器180D，加速度传感器180E，距离传感器180F，接近光传感器180G，指纹传感器180H，温度传感器180J，触摸传感器180K，环境光传感器180L，骨传导传感器180M等。
可以理解的是，本申请实施例示意的结构并不构成对电子设备100的具体限定。在本申请另一些实施例中，电子设备100可以包括比图示更多或更少的部件，或者组合某些部件，或者拆分某些部件，或者不同的部件布置。图示的部件可以以硬件，软件或软件和硬件的组合实现。
处理器110可以包括一个或多个处理单元，例如：处理器110可以包括应用处理器(application processor，AP)，调制解调处理器，图形处理器(graphics processing unit，GPU)，图像信号处理器(image signal processor，ISP)，控制器，存储器，视频编解码器，数字信号处理器(digital signal processor，DSP)，基带处理器，和/或神经网络处理器(neural-network processing unit，NPU)等。其中，不同的处理单元可以是独立的器件，也可以集成在一个或多个处理器中。
其中，控制器可以是电子设备100的神经中枢和指挥中心。控制器可以根据指令操作码和时序信号，产生操作控制信号，完成取指令和执行指令的控制。
处理器110中还可以设置存储器，用于存储指令和数据。在一些实施例中，处理器110中的存储器为高速缓冲存储器。该存储器可以保存处理器110刚用过或循环使用的指令或 数据。如果处理器110需要再次使用该指令或数据，可从存储器中直接调用。避免了重复存取，减少了处理器110的等待时间，因而提高了系统的效率。
在一些实施例中，处理器110可以包括一个或多个接口。接口可以包括集成电路(inter-integrated circuit，I2C)接口，集成电路内置音频(inter-integrated circuit sound，I2S)接口，脉冲编码调制(pulse code modulation，PCM)接口，通用异步收发传输器(universal asynchronous receiver/transmitter，UART)接口，移动产业处理器接口(mobile industry processor interface，MIPI)，通用输入输出(general-purpose input/output，GPIO)接口，用户标识模块(subscriber identity module，SIM)接口，和/或通用串行总线(universal serial bus，USB)接口等。
I2C接口是一种双向同步串行总线，包括一根串行数据线(serial data line，SDA)和一根串行时钟线(derail clock line，SCL)。在一些实施例中，处理器110可以包含多组I2C总线。
I2S接口可以用于音频通信。在一些实施例中，处理器110可以包含多组I2S总线。处理器110可以通过I2S总线与音频模块170耦合，实现处理器110与音频模块170之间的通信。
PCM接口也可以用于音频通信，将模拟信号抽样，量化和编码。在一些实施例中，音频模块170与无线通信模块160可以通过PCM总线接口耦合。
UART接口是一种通用串行数据总线，用于异步通信。该总线可以为双向通信总线。它将要传输的数据在串行通信与并行通信之间转换。在一些实施例中，UART接口通常被用于连接处理器110与无线通信模块160。
MIPI接口可以被用于连接处理器110与显示屏194，摄像头193等外围器件。MIPI接口包括摄像头串行接口(camera serial interface，CSI)，显示屏串行接口(display serial interface，DSI)等。在一些实施例中，处理器110和摄像头193通过CSI接口通信，实现电子设备100的拍摄功能。处理器110和显示屏194通过DSI接口通信，实现电子设备100的显示功能。
GPIO接口可以通过软件配置。GPIO接口可以被配置为控制信号，也可被配置为数据信号。在一些实施例中，GPIO接口可以用于连接处理器110与摄像头193，显示屏194，无线通信模块160，音频模块170，传感器模块180等。GPIO接口还可以被配置为I2C接口，I2S接口，UART接口，MIPI接口等。
USB接口130是符合USB标准规范的接口，具体可以是Mini USB接口，Micro USB接口，USB Type C接口等。USB接口130可以用于连接充电器为电子设备100充电，也可以用于电子设备100与外围设备之间传输数据。也可以用于连接耳机，通过耳机播放音频。该接口还可以用于连接其他电子设备，例如AR设备等。
可以理解的是，本申请实施例示意的各模块间的接口连接关系，只是示意性说明，并不构成对电子设备100的结构限定。在本申请另一些实施例中，电子设备100也可以采用上述实施例中不同的接口连接方式，或多种接口连接方式的组合。
充电管理模块140用于从充电器接收充电输入。其中，充电器可以是无线充电器，也可以是有线充电器。在一些有线充电的实施例中，充电管理模块140可以通过USB接口130接收有线充电器的充电输入。在一些无线充电的实施例中，充电管理模块140可以通过电子设备100的无线充电线圈接收无线充电输入。充电管理模块140为电池142充电的 同时，还可以通过电源管理模块141为电子设备供电。
电源管理模块141用于连接电池142，充电管理模块140与处理器110。电源管理模块141接收电池142和/或充电管理模块140的输入，为处理器110，内部存储器121，外部存储器，显示屏194，摄像头193，和无线通信模块160等供电。电源管理模块141还可以用于监测电池容量，电池循环次数，电池健康状态(漏电，阻抗)等参数。在其他一些实施例中，电源管理模块141也可以设置于处理器110中。在另一些实施例中，电源管理模块141和充电管理模块140也可以设置于同一个器件中。
电子设备100的无线通信功能可以通过天线1，天线2，移动通信模块150，无线通信模块160，调制解调处理器以及基带处理器等实现。
天线1和天线2用于发射和接收电磁波信号。图1中的天线1和天线2的结构仅为一种示例。移动通信模块150可以提供应用在电子设备100上的包括2G/3G/4G/5G等无线通信的解决方案。移动通信模块150可以包括至少一个滤波器，开关，功率放大器，低噪声放大器(low noise amplifier，LNA)等。移动通信模块150可以由天线1接收电磁波，并对接收的电磁波进行滤波，放大等处理，传送至调制解调处理器进行解调。移动通信模块150还可以对经调制解调处理器调制后的信号放大，经天线1转为电磁波辐射出去。
调制解调处理器可以包括调制器和解调器。其中，调制器用于将待发送的低频基带信号调制成中高频信号。解调器用于将接收的电磁波信号解调为低频基带信号。随后解调器将解调得到的低频基带信号传送至基带处理器处理。低频基带信号经基带处理器处理后，被传递给应用处理器。应用处理器通过音频设备(不限于扬声器170A，受话器170B等)输出声音信号，或通过显示屏194显示图像或视频。在一些实施例中，调制解调处理器可以是独立的器件。在另一些实施例中，调制解调处理器可以独立于处理器110，与移动通信模块150或其他功能模块设置在同一个器件中。
无线通信模块160可以提供应用在电子设备100上的包括无线局域网(wireless local area networks，WLAN)(如无线保真(wireless fidelity，Wi-Fi)网络)，蓝牙(bluetooth，BT)，全球导航卫星系统(global navigation satellite system，GNSS)，调频(frequency modulation，FM)，近距离无线通信技术(near field communication，NFC)，红外技术(infrared，IR)等无线通信的解决方案。无线通信模块160可以是集成至少一个通信处理模块的一个或多个器件。无线通信模块160经由天线2接收电磁波，将电磁波信号调频以及滤波处理，将处理后的信号发送到处理器110。无线通信模块160还可以从处理器110接收待发送的信号，对其进行调频，放大，经天线2转为电磁波辐射出去。
电子设备100通过GPU，显示屏194，以及应用处理器等实现显示功能。GPU为图像处理的微处理器，连接显示屏194和应用处理器。GPU用于执行数学和几何计算，用于图形渲染。处理器110可包括一个或多个GPU，其执行程序指令以生成或改变显示信息。
显示屏194用于显示图像，视频等。显示屏194包括显示面板。显示面板可以采用液晶显示屏(liquid crystal display，LCD)，有机发光二极管(organic light-emitting diode，OLED)，有源矩阵有机发光二极体或主动矩阵有机发光二极体(active-matrix organic light emitting diode的，AMOLED)，柔性发光二极管(flex light-emitting diode，FLED)，Miniled，MicroLed，Micro-oLed，量子点发光二极管(quantum dot light emitting diodes，QLED)等。在一些实施例中，电子设备100可以包括1个或N个显示屏194，N为大于1的正整数。
电子设备100可以通过ISP，摄像头193，视频编解码器，GPU，显示屏194以及应用处理器等实现拍摄功能。
ISP用于处理摄像头193反馈的数据。例如，拍照时，打开快门，光线通过镜头被传递到摄像头感光元件上，光信号转换为电信号，摄像头感光元件将电信号传递给ISP处理，转化为肉眼可见的图像。ISP还可以对图像的噪点，亮度，肤色进行算法优化。ISP还可以对拍摄场景的曝光，色温等参数优化。在一些实施例中，ISP可以设置在摄像头193中。
摄像头193用于捕获静态图像或视频。物体通过镜头生成光学图像投射到感光元件。感光元件可以是电荷耦合器件(charge coupled device，CCD)或互补金属氧化物半导体(complementary metal-oxide-semiconductor，CMOS)光电晶体管。感光元件把光信号转换成电信号，之后将电信号传递给ISP转换成数字图像信号。ISP将数字图像信号输出到DSP加工处理。DSP将数字图像信号转换成标准的RGB，YUV等格式的图像信号。在一些实施例中，电子设备100可以包括1个或N个摄像头193，N为大于1的正整数。在一些实施例中，摄像头193可以包括可见光摄像头，用于采集可见光图像。在一些实施例中，摄像头193还可以包括飞行时间(time of flight，TOF)镜头。其中，TOF是物体、粒子或波(例如声波、电磁波等)在介质中传播一段距离所用的时间的测量值。TOF镜头可以发射波，并接收上述波遇到被拍摄物体后、被上述被拍摄物体反射回来的波。从而电子设备可以获得TOF镜头从发射波到接收反射波的时间差，和/或TOF镜头发射的波和接收到的反射波之间的相位差。进而电子设备根据上述时间差和/或相位差计算得到电子设备与被摄物体之间的距离，形成一组距离深度数据，从而得到包含被摄物体深度信息的3D模型或3D图像。可选地，上述波可以是红外光、激光脉冲、超声波等。
数字信号处理器用于处理数字信号，除了可以处理数字图像信号，还可以处理其他数字信号。例如，当电子设备100在频点选择时，数字信号处理器用于对频点能量进行傅里叶变换等。
视频编解码器用于对数字视频压缩或解压缩。电子设备100可以支持一种或多种视频编解码器。这样，电子设备100可以播放或录制多种编码格式的视频，例如：动态图像专家组(moving picture experts group，MPEG)1，MPEG2，MPEG3，MPEG4等。
NPU为神经网络(neural-network，NN)计算处理器，通过借鉴生物神经网络结构，例如借鉴人脑神经元之间传递模式，对输入信息快速处理，还可以不断的自学习。通过NPU可以实现电子设备100的智能认知等应用，例如：图像识别，人脸识别，语音识别，文本理解等。
外部存储器接口120可以用于连接外部存储卡，例如Micro SD卡，实现扩展电子设备100的存储能力。外部存储卡通过外部存储器接口120与处理器110通信，实现数据存储功能。例如将音乐，视频等文件保存在外部存储卡中。
内部存储器121可以用于存储计算机可执行程序代码，可执行程序代码包括指令。处理器110通过运行存储在内部存储器121的指令，从而执行电子设备100的各种功能应用以及数据处理。内部存储器121可以包括存储程序区和存储数据区。其中，存储程序区可存储操作系统，至少一个功能所需的应用程序(比如声音播放功能，图像播放功能等)等。存储数据区可存储电子设备100使用过程中所创建的数据(比如音频数据，电话本等)等。此外，内部存储器121可以包括高速随机存取存储器，还可以包括非易失性存储器，例如 至少一个磁盘存储器件，闪存器件，通用闪存存储器(universal flash storage，UFS)等。
电子设备100可以通过音频模块170，扬声器170A，受话器170B，麦克风170C，耳机接口170D，以及应用处理器等实现音频功能。例如音乐播放，录音等。
压力传感器180A用于感受压力信号，可以将压力信号转换成电信号。在一些实施例中，压力传感器180A可以设置于显示屏194。压力传感器180A的种类很多，如电阻式压力传感器，电感式压力传感器，电容式压力传感器等。电容式压力传感器可以是包括至少两个具有导电材料的平行板。当有力作用于压力传感器180A，电极之间的电容改变。电子设备100根据电容的变化确定压力的强度。当有触摸操作作用于显示屏194，电子设备100根据压力传感器180A检测触摸操作强度。电子设备100也可以根据压力传感器180A的检测信号计算触摸的位置。在一些实施例中，作用于相同触摸位置，但不同触摸操作强度的触摸操作，可以对应不同的操作指令。例如：当有触摸操作强度小于第一压力阈值的触摸操作作用于短消息应用图标时，执行查看短消息的指令。当有触摸操作强度大于或等于第一压力阈值的触摸操作作用于短消息应用图标时，执行新建短消息的指令。
陀螺仪传感器180B可以用于确定电子设备100的运动姿态。在一些实施例中，可以通过陀螺仪传感器180B确定电子设备100围绕三个轴(即，x，y和z轴)的角速度。陀螺仪传感器180B可以用于拍摄防抖。示例性的，当按下快门，陀螺仪传感器180B检测电子设备100抖动的角度，根据角度计算出镜头模组需要补偿的距离，让镜头通过反向运动抵消电子设备100的抖动，实现防抖。陀螺仪传感器180B还可以用于导航，体感游戏场景。
气压传感器180C用于测量气压。在一些实施例中，电子设备100通过气压传感器180C测得的气压值计算海拔高度，辅助定位和导航。
磁传感器180D包括霍尔传感器。电子设备100可以利用磁传感器180D检测翻盖皮套的开合。在一些实施例中，当电子设备100是翻盖机时，电子设备100可以根据磁传感器180D检测翻盖的开合。进而根据检测到的皮套的开合状态或翻盖的开合状态，设置翻盖自动解锁等特性。
加速度传感器180E可检测电子设备100在各个方向上(一般为三轴)加速度的大小。当电子设备100静止时可检测出重力的大小及方向。还可以用于识别电子设备姿态，应用于横竖屏切换，计步器等应用。
距离传感器180F，用于测量距离。电子设备100可以通过红外或激光测量距离。在一些实施例中，拍摄场景，电子设备100可以利用距离传感器180F测距以实现快速对焦。
接近光传感器180G可以包括例如发光二极管(LED)和光检测器，例如光电二极管。发光二极管可以是红外发光二极管。电子设备100通过发光二极管向外发射红外光。电子设备100使用光电二极管检测来自附近物体的红外反射光。当检测到充分的反射光时，可以确定电子设备100附近有物体。当检测到不充分的反射光时，电子设备100可以确定电子设备100附近没有物体。电子设备100可以利用接近光传感器180G检测用户手持电子设备100贴近耳朵通话，以便自动熄灭屏幕达到省电的目的。接近光传感器180G也可用于皮套模式，口袋模式自动解锁与锁屏。
环境光传感器180L用于感知环境光亮度。电子设备100可以根据感知的环境光亮度自适应调节显示屏194亮度。环境光传感器180L也可用于拍照时自动调节白平衡。环境光传感器180L还可以与接近光传感器180G配合，检测电子设备100是否在口袋里，以防 误触。
指纹传感器180H用于采集指纹。电子设备100可以利用采集的指纹特性实现指纹解锁，访问应用锁，指纹拍照，指纹接听来电等。
温度传感器180J用于检测温度。在一些实施例中，电子设备100利用温度传感器180J检测的温度，执行温度处理策略。
触摸传感器180K，也称“触控面板”。触摸传感器180K可以设置于显示屏194，由触摸传感器180K与显示屏194组成触摸屏，也称“触控屏”。触摸传感器180K用于检测作用于其上或附近的触摸操作。触摸传感器可以将检测到的触摸操作传递给应用处理器，以确定触摸事件类型。可以通过显示屏194提供与触摸操作相关的视觉输出。在另一些实施例中，触摸传感器180K也可以设置于电子设备100的表面，与显示屏194所处的位置不同。
骨传导传感器180M可以获取振动信号。在一些实施例中，骨传导传感器180M可以获取人体声部振动骨块的振动信号。骨传导传感器180M也可以接触人体脉搏，接收血压跳动信号。在一些实施例中，骨传导传感器180M也可以设置于耳机中，结合成骨传导耳机。音频模块170可以基于骨传导传感器180M获取的声部振动骨块的振动信号，解析出语音信号，实现语音功能。应用处理器可以基于骨传导传感器180M获取的血压跳动信号解析心率信息，实现心率检测功能。
在一些实施例中，电子设备100还包括高光谱传感器，该高光谱传感器可以为点阵式传感器或者线阵式传感器，点阵式传感器可以采集点阵高光谱数据，线阵式传感器可以采集线阵高光谱数据。
按键190包括开机键，音量键等。按键190可以是机械按键。也可以是触摸式按键。电子设备100可以接收按键输入，产生与电子设备100的用户设置以及功能控制有关的键信号输入。
马达191可以产生振动提示。马达191可以用于来电振动提示，也可以用于触摸振动反馈。例如，作用于不同应用(例如拍照，音频播放等)的触摸操作，可以对应不同的振动反馈效果。作用于显示屏194不同区域的触摸操作，马达191也可对应不同的振动反馈效果。不同的应用场景(例如：时间提醒，接收信息，闹钟，游戏等)也可以对应不同的振动反馈效果。触摸振动反馈效果还可以支持自定义。
指示器192可以是指示灯，可以用于指示充电状态，电量变化，也可以用于指示消息，未接来电，通知等。
SIM卡接口195用于连接SIM卡。SIM卡可以通过插入SIM卡接口195，或从SIM卡接口195拔出，实现和电子设备100的接触和分离。电子设备100可以支持1个或N个SIM卡接口，N为大于1的正整数。
电子设备100的软件系统可以采用分层架构，事件驱动架构，微核架构，微服务架构，或云架构。本申请实施例以分层架构的Android系统为例，示例性说明电子设备100的软件结构。
图2是本申请实施例的电子设备100的软件结构框图。分层架构将软件分成若干个层，每一层都有清晰的角色和分工。层与层之间通过软件接口通信。在一些实施例中，将Android系统分为四层，从上至下分别为应用程序层，应用程序框架层，安卓运行时(Android runtime) 和系统库，以及内核层。应用程序层可以包括一系列应用程序包。
如图2所示，应用程序包可以包括相机，图库，日历，通话，地图，导航，WLAN，蓝牙，音乐，视频，短信息等应用程序。
应用程序框架层为应用程序层的应用程序提供应用编程接口(application programming interface，API)和编程框架。应用程序框架层包括一些预先定义的函数。
如图2所示，应用程序框架层可以包括窗口管理器，内容提供器，视图系统，电话管理器，资源管理器，通知管理器等。
窗口管理器用于管理窗口程序。窗口管理器可以获取显示屏大小，判断是否有状态栏，锁定屏幕，截取屏幕等。
内容提供器用来存放和获取数据，并使这些数据可以被应用程序访问。数据可以包括视频，图像，音频，拨打和接听的电话，浏览历史和书签，电话簿等。
视图系统包括可视控件，例如显示文字的控件，显示图片的控件等。视图系统可用于构建应用程序。显示界面可以由一个或多个视图组成的。例如，包括短信通知图标的显示界面，可以包括显示文字的视图以及显示图片的视图。
电话管理器用于提供电子设备100的通信功能。例如通话状态的管理(包括接通，挂断等)。
资源管理器为应用程序提供各种资源，比如本地化字符串，图标，图片，布局文件，视频文件等等。
通知管理器使应用程序可以在状态栏中显示通知信息，可以用于传达告知类型的消息，可以短暂停留后自动消失，无需用户交互。比如通知管理器被用于告知下载完成，消息提醒等。通知管理器还可以是以图表或者滚动条文本形式出现在系统顶部状态栏的通知，例如后台运行的应用程序的通知，还可以是以对话窗口形式出现在屏幕上的通知。例如在状态栏提示文本信息，发出提示音，电子设备振动，指示灯闪烁等。
Android runtime包括核心库和虚拟机。Android runtime负责安卓系统的调度和管理。
核心库包含两部分：一部分是java语言需要调用的功能函数，另一部分是安卓的核心库。
应用程序层和应用程序框架层运行在虚拟机中。虚拟机将应用程序层和应用程序框架层的java文件执行为二进制文件。虚拟机用于执行对象生命周期的管理，堆栈管理，线程管理，安全和异常的管理，以及垃圾回收等功能。
系统库可以包括多个功能模块。例如：表面管理器(surface manager)，媒体库(media libraries)，三维图形处理库(例如：OpenGL ES)，2D图形引擎(例如：SGL)等。
表面管理器用于对显示子系统进行管理，并且为多个应用程序提供了2D和3D图层的融合。
媒体库支持多种常用的音频，视频格式回放和录制，以及静态图像文件等。媒体库可以支持多种音视频编码格式，例如:MPEG4，H.264，MP3，AAC，AMR，JPG，PNG等。
三维图形处理库用于实现三维图形绘图，图像渲染，合成，和图层处理等。
2D图形引擎是2D绘图的绘图引擎。
内核层是硬件和软件之间的层。内核层至少包含显示驱动，摄像头驱动，音频驱动，传感器驱动。
为了便于理解，本申请以下实施例将以具有图1和图2所示结构的电子设备为例，结合附图对本申请实施例提供的生成图像的方法进行具体阐述。
本申请实施例提供的生成图像的方法可以应用于图3所示的系统架构中，首先结合图3介绍本申请实施例的应用场景。
当用户手持电子设备面向目标区域进行多次拍摄时，电子设备可采集到多个可见光图像和多个高光谱数据，该高光谱数据可以为点阵高光谱数据或者线阵高光谱数据。为方便描述，下面以线阵式高光谱数据为例进行说明。
电子设备获取到多个可见光图像后，可以对该多个可见光图像进行图像配准，以获得图像配准时的配准参数。以两个可见光图像A和B为例，如图3所示，电子设备可以首先对图像A和图像B进行图像预处理，然后提取图像A中的特征点和图像B中的特征点。继而电子设备对图像A中的特征点和图像B中的特征点进行匹配，得到特征点对。最后电子设备可以根据特征点对计算得到图像A和图像B之间空间坐标变换的配准参数，如图3所示，上述配准参数可以为空间坐标变换矩阵，简称为变换矩阵。其中，图像预处理包括但不限于基于摄像头内参和畸变参数对图像进行校正的过程。图像的特征点可以通过提取图像中像素点的特征来确定，像素点的特征可以包括点特征、线特征和面特征；点特征的提取算法可以包括但不限于尺度不变特征变换(scale-invariant feature transform，SIFT)算法、SURF(speeded up robust features)算法和ORB(oriented fast and rotated brief)算法，线特征的提取算法可以包括但不限于log算法和canny算法，面特征的提取算法可以包括但不限于区域分割算法。特征点匹配的算法包括但不限于欧式距离算法、最邻近算法(k-nearestneighbor，KNN)、汉明距离算法和相似性度量算法。求解变换矩阵的算法包括但不限于随机抽样一致(random sample consensus，RANSAC)算法、RHO算法、最小中值(LMEDS)算法和最小二乘算法。
在本申请实施例提供的生成图像的方法中，电子设备利用该配准参数对线阵高光谱数据进行坐标转换，并对进行了坐标转换后的线阵高光谱数据进行拼接处理，从而得到面阵高光谱图像，实现无需借助外部机械设备就可生成面阵高光谱图像的技术效果。
此外，电子设备还可以利用上述配准参数对可见光图像中的像素点坐标进行坐标转换，以得到图像配准后的可见光图像，并对图像配准后的可见光图像进行拼接。
进一步地，电子设备还可以基于得到的面阵高光谱图像对拼接的可见光图像进行后处理，例如图像去雾(如图3中所示)、图像增强等。
可选地，上述基于面阵高光谱图像对拼接的可见光图像进行去雾处理的过程，可以包括雾浓度估计、图像分区、图像融合及图像滤波等过程，具体实现过程可参见下述方法实施例中的描述。
可选地，电子设备还可以利用TOF镜头的采集数据、硬件传感器的采集数据以及输出的交互提示信息，提高上述配准参数的精度，进而提高得到的面阵高光谱图像的精度。如前所述，通过TOF镜头发射波和接收反射波的过程，电子设备可以计算得到被摄物体的深度信息；硬件传感器的采集数据可以包括电子设备的加速度等。
关于本申请实施例提供的生成图像的方法，下面对其进行具体介绍。图4是本申请实施例提供的一例生成图像的方法的流程示意图，该方法包括：
S101，获取图像采集装置采集的n个第一图像和n个第一光谱数据；其中，一个第一 光谱数据对应一个第一图像，第一光谱数据为点阵高光谱数据或者线阵高光谱数据，n为大于或者等于2的正整数。
本实施例中，图像采集装置包括上述图1实施例中所描述的可见光摄像头和高光谱传感器，可见光摄像头可以采集可见光图像(即第一图像)，高光谱传感器可以采集点阵高光谱数据或者线阵高光谱数据(即第一光谱数据)。用户手持电子设备面向目标区域进行一次拍摄时，电子设备可同时采集一个可见光图像和一个第一光谱数据。因此在连续拍摄或者全景拍摄模式下，电子设备可获取同一场景下目标区域不同角度的多个(如n个)可见光图像和多个第一光谱数据。
其中，为保证第一光谱数据与可见光图像之间的变换相对同步，可以固定目标区域在可见光图像中的位置，并使可见光摄像头的焦距与高光谱传感器的焦距保持一致。摄像头成像的过程通常为世界坐标系向像素坐标系转换的过程，即世界坐标系(3d)-摄像头坐标系(3d)-像平面坐标系(2d)-像素坐标系(2d)。经过这样一级一级的转换之后，目标区域在空间中的坐标即转换为在图像中的像素坐标。可选地，在得到可见光图像后，电子设备还可以基于摄像头内参和畸变参数对可见光图像进行校正预处理。
可选地，在全景拍摄模式下，为使用户尽量将电子设备持平以拍摄得到位于同一水平线的n个可见光图像，可以在电子设备的显示屏上显示一些提示信息。例如，如图5所示，上述提示信息可以是显示在拍摄界面的一条水平方向的中心线，该中心线用于辅助用户在拍摄时尽量使目标区域基于该中心线移动。可选地，上述提示信息还可以包括矩形框，矩形框内可以显示有已拍摄到的图像画面；在电子设备开始拍摄之前，上述矩形框可以位于显示界面的一侧、中心或者中心区域附近；随着电子设备拍摄到越来越多的图像画面，矩形框在中心线方向上的边可以逐渐变长，或者矩形框可以沿着中心线逐渐移动。从而，上述提示信息可用于控制各可见光图像之间的偏差范围，得到质量较高的n个可见光图像。可选地，上述提示信息还可以是显示在电子设备的拍摄界面的一条垂直方向的中心线，或者还可以是同时显示在电子设备的拍摄界面的水平方向的中心线和垂直方向的中心线，进一步提高可见光图像的拍摄质量。
S102，确定第i个第一图像进行图像配准时所需的配准参数，i遍历1至n。
具体地，在获取n个可见光图像后，电子设备可以确定各可见光图像进行图像配准时所需的配准参数，如前所述，该配准参数可以为变换矩阵。基于该变换矩阵可以对可见光图像中的像素点坐标进行坐标变换以完成图像配准；本实施例中，基于该变换矩阵还可以对第一光谱数据进行坐标变换得到第二光谱数据。
在一种可能的实现方式中，对于第i个可见光图像，可以将第i-1个可见光图像作为基准图像进行图像配准，并确定第i个可见光图像对应的配准参数。例如，以第1个可见光图像作为基准图像，将第2个可见光图像配准至第1个可见光图像的空间域上；以第2个可见光图像作为基准图像，将第3个可见光图像配准至第2个可见光图像的空间域上，以此类推，得到每个可见光图像配准时所需的配准参数。
在另一种可能的实现方式中，可以将n个可见光图像中的任意一个图像作为其余n-1个图像的基准图像，并确定其余n-1个可见光图像对应的配准参数。例如，选取第1个可见光图像作为基准图像，将第2个至第n个可见光图像都配准至第1个可见光图像的空间域上。
S103，利用配准参数对第i个第一光谱数据进行处理，得到第i个第二光谱数据；其中，第i个第一光谱数据为对应于第i个第一图像的光谱数据。
其中，因每个第一光谱数据对应一个可见光图像，且第一光谱数据与可见光图像之间的变换相对同步，因此电子设备可以采用可见光图像对应的配准参数对第一光谱数据进行处理，即采用第i个可见光图像的配准参数，对与第i个可见光图像对应的第一光谱数据进行处理，得到第二光谱数据。作为示例而非限定的，可以采用第i个可见光图像的配准参数，对与第i个可见光图像对应的第一光谱数据进行坐标变换，使第一光谱数据都位于同一基准坐标系下，得到第二光谱数据。
S104，根据得到的n个第二光谱数据，生成第二图像，第二图像为面阵高光谱图像。
具体地，上述针对每个第一光谱数据都得到了对应的第二光谱数据，则共有n个第二光谱数据，这n个第二光谱数据仍为点阵高光谱数据或线阵高光谱数据，那么电子设备可以对n个第二光谱数据进行拼接，以生成面阵高光谱图像。
上述实施例中，电子设备采用可见光图像配准时所需的配准参数，对点阵高光谱数据或者线阵高光谱数据进行变换处理，最终拼接为面阵高光谱图像。该方法无需借助复杂的机械设备进行辅助就可以生成面阵高光谱图像，适用性较强，大大提升了电子设备生成面阵高光谱图像的能力。
在一个实施例中，如图6所示，上述S102中为第i个第一图像确定图像配准时所需的配准参数的过程可以包括：
S201，选取n个第一图像中的第j个第一图像作为第i个第一图像的基准图像，其中，第j个第一图像不同于第i个第一图像。
具体地，第i个可见光图像的基准图像(即第j个可见光图像)的确定方式可以参见上述实施例的描述，例如选取第i-1个可见光图像或者其余任意一个可见光图像作为第i个可见光图像的基准图像。进一步地，第j个可见光图像还可以是与第i个可见光图像相邻的前一帧图像，即j可以为i-1，那么，电子设备在利用第i个第一图像的配准参数对第i个第一图像进行图像配准，得到配准后的第i个第一图像之后，可以将该配准后的第i个第一图像作为第i+1个第一图像的基准图像。例如，以第1个可见光图像作为基准图像，将第2个可见光图像配准至第1个可见光图像的空间域上后，再以配准后的第2个可见光图像为基准图像，将第3个可见光图像配准至其空间域上，然后再以配准后的第3个可见光图像为基准图像，将第4个可见光图像配准至其空间域上，以此类推，由此可使n个可见光图像配准至同一空间域上，提高了各可见光图像进行图像配准时的精度。
S202，对第j个第一图像进行特征提取得到第一特征点、对第i个第一图像进行特征提取得到第二特征点，并根据第一特征点与第二特征点确定匹配的特征点对。
S203，根据匹配的特征点对的坐标信息，确定第i个第一图像的配准参数。
具体地，在确定了第i个可见光图像的基准图像(即第j个可见光图像)后，电子设备可以分别对两个可见光图像进行特征提取，以确定匹配的特征点对，并根据特征点对的坐标信息确定第i个可见光图像的配准参数。
作为示例而非限定的，以第1个可见光图像A(基准图像)和第2个可见光图像B进行图像配准为例介绍确定配准参数的过程：首先提取图像A中各像素点的特征和图像B中各像素点的特征，以确定图像A中的第一特征点和图像B中的第二特征点，可选地，像素 点特征可以包括点特征、线特征和面特征。然后，基于图像A的第一特征点和图像B的第二特征点进行特征点匹配，确定匹配的特征点对。最后，基于匹配的特征点对的坐标信息，确定图像A与图像B之间的变换矩阵。例如，假设(a，b)为一个匹配的特征点对，其中a为图像A的一个特征点，b为图像B的一个特征点，假设a的坐标为(x 1，y 1)，b的坐标为(x 2，y 2)，则存在一个矩阵H使得(x 1,y 1)＝H×(x 2,y 2)，那么H即为图像B向图像A进行配准时采用的变换矩阵，即配准参数。需要说明的是，关于各特征的提取算法、特征点对的匹配算法以及求解变换矩阵的算法可以参见上述图3实施例中的描述，在此不再赘述。
上述实施例中，电子设备通过对基准图像和第i个可见光图像进行特征提取以得到匹配的特征点对，进而确定第i个可见光图像的配准参数，为后续对点阵高光谱数据或者线阵高光谱数据进行变换处理提供数据准备，进而无需借助复杂的机械设备进行辅助，适用性较强。
另外，为提高上述实施例中所确定的配准参数的精度，在一个实施例中，上述图像采集装置还包括上述图1实施例中所描述的TOF镜头，通过TOF镜头的采集数据可以计算得到可见光图像中各像素点的三维坐标信息，相应的，电子设备可以获取到上述第一特征点的三维坐标信息和第二特征点的坐标信息，则上述S202中根据第一特征点与第二特征点确定匹配的特征点对的过程可以包括：根据第一特征点的三维坐标信息和第二特征点的三维坐标信息，采用上述实施例中的欧式距离算法、KNN算法、汉明距离算法或者相似性度量算法等算法对第一特征点和第二特征点进行匹配，确定匹配的特征点对，以提高所匹配的特征点对的精度，进而提高配准参数的精度。
对于一种实际应用场景，在用户持电子设备进行拍摄过程中，可能会由于抖动或其他因素导致所拍摄的可见光图像中出现阴影等现象，因此，本申请实施例还可以先对采集的可见光图像进行优化，优化之后再执行S202中特征提取得到特征点的过程。可选地，电子设备中可配置有惯性传感器，如加速度计和陀螺仪等传感器，该惯性传感器可实时采集表征电子设备运动状态的传感器数据，则图像采集装置在采集第i个可见光图像时，惯性传感器也会采集到对应时刻的传感器数据，在采集第j个可见光图像时，惯性传感器也会采集到对应时刻的传感器数据；那么，电子设备便可以根据在图像采集装置采集第j个可见光图像时，惯性传感器采集的传感器数据对第j个可见光图像进行图像优化，得到优化后的第j个可见光图像；根据在图像采集装置采集第i个可见光图像时，惯性传感器采集的传感器数据对第i个可见光图像进行图像优化，得到优化后的第i个可见光图像。
具体地，以对第i个可见光图像进行图像优化为例进行说明。惯性传感器可以采集得到电子设备的三轴角速度和三轴加速度。电子设备可以利用卡尔曼滤波方法对三轴角速度和三轴加速度进行滤波后，使用欧拉动力学方程式将三轴角速度转换为欧拉角，使用匀加速运动公式将三轴加速度转换成三轴位移，从而获得旋转矩阵和平移矩阵。接下来，电子设备可以根据旋转矩阵和平移矩阵计算第i个可见光图像的背景光流，并采用使用RANSAC算法对第i个可见光图像的背景光流进行鲁棒性估计，以将背景光流中存在的运动光流剔除掉，完成对第i个可见光图像的图像优化。电子设备对第j个可见光图像进行图像优化的过程与对第i个可见光图像进行图像优化的过程类似，在此不再赘述。
上述实施例中，电子设备先对可见光图像进行图像优化后再执行特征提取的过程，可 提高得到的图像的特征点的准确性，进而提高匹配的特征点对的准确性，继而提高得到的配准参数的精度，在此基础上对点阵高光谱数据或者线阵高光谱数据进行变换处理，可提高得到的面阵高光谱图像的精度。
对于另一实际应用场景，若图像采集装置采集可见光图像时外部环境较差，如有雾天气，则采集得到的可见光图像清晰度不高、质量较差，影响可见光图像的展示效果，那么本实施例中电子设备还可以基于得到的第二图像，即面阵高光谱图像，对可见光图像进行去雾处理或图像增强处理，以提高可见光图像的清晰度，进而提高可见光图像的图像质量。
在一种可能的实现方式中，电子设备可以基于第二图像，采用预设的算法对第i个可见光图像进行去雾处理，得到去雾后的第i个可见光图像。
具体地，上述预设的算法可以为可见光近红外图像融合算法，电子设备可以分别从第i个可见光图像的蓝光通道和第二图像中提取相对应像素的亮度值，根据相对应像素的亮度值的差异，确定雾浓度分布，然后按照雾浓度分布，将第二图像和第i个可见光图像中相对应像素的亮度值分别融合，得到去雾亮度值，最后根据各去雾亮度值和第i个可见光图像中对应的色彩值，生成去雾后的第i个可见光图像。可选地，电子设备还可以继续对第i个可见光图像进行图像滤波，进一步提高可见光图像的图像质量。
在另一种可能的实现方式中，电子设备还可以基于第二图像，采用预设的算法对目标图像进行去雾处理，得到去雾后的目标图像，该目标图像为对n个可见光图像进行图像配准后生成的拼接图像。
具体地，电子设备为n个可见光图像确定了图像配准时所需的配准参数后，可以基于第i个可见光图像的配准参数，对第i个可见光图像的像素点坐标进行坐标变换，得到配准后的第i个可见光图像，然后将图像配准后的n个可见光图像进行图像拼接，生成拼接可见光图像；例如将多个部分可见光图像拼接为全景可见光图像。接下来，再采用上述实施例中的可见光近红外图像融合算法对拼接可见光图像和第二图像进行融合，得到去雾后的拼接可见光图像。
在又一种可能的实现方式中，电子设备还可以提取可见光图像中特征点的像素值、以及第二图像中对应点的像素值，然后根据第二图像中对应点的像素值，对可见光图像中特征点的像素值进行融合，以对可见光图像中的特征区域进行图像增强，加强可见光图像的特征识别效果。
上述实施例中，在面阵高光谱图像的基础上，基于高光谱图像与可见光图像融合方法，对含雾的可见光图像进行去雾处理，可以提高可见光图像的清晰度，进而提高图像质量。
为更好的理解本申请实施例提供的生成图像的方法的整个过程，还可以再参见如图7所示的实施例过程，其中各步骤的实现过程可以参见上述实施例的描述，其实现原理和技术效果类似，在此不再赘述。
上文详细介绍了本申请实施例提供的生成图像的方法的示例。可以理解的是，电子设备为了实现上述功能，其包含了执行各个功能相应的硬件和/或软件模块。本领域技术人员应该很容易意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，本申请能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行，取决于技术方案的特定应用和设计约束条件。本领域技术人员可以结合实施例对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应 认为超出本申请的范围。
本申请实施例可以根据上述方法示例对电子设备进行功能模块的划分，例如，可以对应各个功能划分为各个功能模块，例如检测单元、处理单元、显示单元等，也可以将两个或两个以上的功能集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。需要说明的是，本申请实施例中对模块的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式。
需要说明的是，上述方法实施例涉及的各步骤的所有相关内容均可以援引到对应功能模块的功能描述，在此不再赘述。
本实施例提供的电子设备，用于执行上述生成图像的方法，因此可以达到与上述实现方法相同的效果。
在采用集成的单元的情况下，电子设备还可以包括处理模块、存储模块和通信模块。其中，处理模块可以用于对电子设备的动作进行控制管理。存储模块可以用于支持电子设备执行存储程序代码和数据等。通信模块，可以用于支持电子设备与其他设备的通信。
其中，处理模块可以是处理器或控制器。其可以实现或执行结合本申请公开内容所描述的各种示例性的逻辑方框，模块和电路。处理器也可以是实现计算功能的组合，例如包含一个或多个微处理器组合，数字信号处理(digital signal processing，DSP)和微处理器的组合等等。存储模块可以是存储器。通信模块具体可以为射频电路、蓝牙芯片、Wi-Fi芯片等与其他电子设备交互的设备。
在一个实施例中，当处理模块为处理器，存储模块为存储器时，本实施例所涉及的电子设备可以为具有图1所示结构的设备。
本申请实施例还提供了一种计算机可读存储介质，计算机可读存储介质中存储了计算机程序，当计算机程序被处理器执行时，使得处理器执行上述任一实施例的生成图像的方法。
本申请实施例还提供了一种计算机程序产品，当该计算机程序产品在计算机上运行时，使得计算机执行上述相关步骤，以实现上述实施例中的生成图像的方法。
另外，本申请的实施例还提供一种装置，这个装置具体可以是芯片，组件或模块，该装置可包括相连的处理器和存储器；其中，存储器用于存储计算机执行指令，当装置运行时，处理器可执行存储器存储的计算机执行指令，以使芯片执行上述各方法实施例中的生成图像的方法。
其中，本实施例提供的电子设备、计算机可读存储介质、计算机程序产品或芯片均用于执行上文所提供的对应的方法，因此，其所能达到的有益效果可参考上文所提供的对应的方法中的有益效果，此处不再赘述。
通过以上实施方式的描述，所属领域的技术人员可以了解到，为描述的方便和简洁，仅以上述各功能模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能模块完成，即将装置的内部结构划分成不同的功能模块，以完成以上描述的全部或者部分功能。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以 结合或者可以集成到另一个装置，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是一个物理单元或多个物理单元，即可以位于一个地方，或者也可以分布到多个不同地方。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个可读取存储介质中。基于这样的理解，本申请实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该软件产品存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(read only memory，ROM)、随机存取存储器(random access memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上内容，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以权利要求的保护范围为准。

Claims (13)

  1. 一种生成图像的方法，应用于包括图像采集装置的电子设备，其特征在于，所述方法包括：
    获取所述图像采集装置采集的n个第一图像和n个第一光谱数据；其中，一个所述第一光谱数据对应一个所述第一图像，所述第一光谱数据为点阵高光谱数据或者线阵高光谱数据，所述n为大于或者等于2的正整数；
    确定第i个第一图像进行图像配准时所需的配准参数，所述i遍历1至n；
    利用所述配准参数对第i个第一光谱数据进行处理，得到第i个第二光谱数据；其中，所述第i个第一光谱数据为对应于所述第i个第一图像的光谱数据，所述i遍历1至n；
    根据得到的n个所述第二光谱数据，生成第二图像，所述第二图像为面阵高光谱图像。
  2. 根据权利要求1所述的方法，其特征在于，所述确定所述第i个第一图像进行图像配准时所需的所述配准参数，具体包括：
    选取所述n个第一图像中的第j个第一图像作为所述第i个第一图像的基准图像，其中，所述第j个第一图像不同于所述第i个第一图像；
    对所述第j个第一图像进行特征提取得到第一特征点、对所述第i个第一图像进行特征提取得到第二特征点，并根据所述第一特征点与所述第二特征点确定匹配的特征点对；
    根据所述匹配的特征点对的坐标信息，确定所述第i个第一图像的所述配准参数。
  3. 根据权利要求2所述的方法，其特征在于，所述第j个第一图像为与所述第i个第一图像相邻的前一帧图像，所述方法还包括：
    利用所述第i个第一图像的所述配准参数，对所述第i个第一图像进行图像配准，得到配准后的第i个第一图像；其中，所述配准后的第i个第一图像用作第i+1个第一图像的基准图像。
  4. 根据权利要求2或3所述的方法，其特征在于，所述图像采集装置包括TOF镜头，所述根据所述第一特征点与所述第二特征点确定所述匹配的特征点对，具体包括：
    获取所述第一特征点的第一三维坐标信息和所述第二特征点的第二三维坐标信息；其中，所述第一三维坐标信息和所述第二三维坐标信息均为根据所述TOF镜头的采集数据进行计算所得到的；
    根据所述第一三维坐标信息和所述第二三维坐标信息，对所述第一特征点和所述第二特征点进行匹配，确定所述匹配的特征点对。
  5. 根据权利要求2-4任一项所述的方法，其特征在于，所述电子设备中配置有惯性传感器，在所述对所述第j个第一图像进行特征提取得到所述第一特征点、对所述第i个第一图像进行特征提取得到所述第二特征点之前，所述方法还包括：
    根据所述图像采集装置采集所述第j个第一图像时、所述惯性传感器采集的传感器数据，对所述第j个第一图像进行图像优化，得到优化后的第j个第一图像；
    根据所述图像采集装置采集所述第i个第一图像时、所述惯性传感器采集的传感器数据，对所述第i个第一图像进行图像优化，得到优化后的第i个第一图像。
  6. 根据权利要求1-5任一项所述的方法，其特征在于，所述方法还包括：
    基于所述第二图像，采用预设算法对目标图像进行去雾处理，得到去雾后的目标图像； 其中，所述目标图像为所述第i个第一图像，或者所述目标图像为对所述n个第一图像进行图像配准后生成的拼接图像。
  7. 一种电子设备，其特征在于，包括：
    图像采集装置；
    一个或多个处理器；
    一个或多个存储器；
    安装有多个应用程序的模块；
    所述存储器存储有一个或多个程序，当所述一个或者多个程序被所述处理器执行时，使得所述电子设备执行如下步骤：
    获取所述图像采集装置采集的n个第一图像和n个第一光谱数据；其中，一个所述第一光谱数据对应一个所述第一图像，所述第一光谱数据为点阵高光谱数据或者线阵高光谱数据，所述n为大于或者等于2的正整数；
    确定第i个第一图像进行图像配准时所需的配准参数，所述i遍历1至n；
    利用所述配准参数对第i个第一光谱数据进行处理，得到第i个第二光谱数据；其中，所述第i个第一光谱数据为对应于所述第i个第一图像的光谱数据，所述i遍历1至n；
    根据得到的n个所述第二光谱数据，生成第二图像，所述第二图像为面阵高光谱图像。
  8. 根据权利要求7所述的电子设备，其特征在于，当所述一个或者多个程序被所述处理器执行时，使得所述电子设备执行如下步骤：
    选取所述n个第一图像中的第j个第一图像作为所述第i个第一图像的基准图像，其中，所述第j个第一图像不同于所述第i个第一图像；
    对所述第j个第一图像进行特征提取得到第一特征点、对所述第i个第一图像进行特征提取得到第二特征点，并根据所述第一特征点与所述第二特征点确定匹配的特征点对；
    根据所述匹配的特征点对的坐标信息，确定所述第i个第一图像的所述配准参数。
  9. 根据权利要求8所述的电子设备，其特征在于，所述第j个第一图像为与所述第i个第一图像相邻的前一帧图像，当所述一个或者多个程序被所述处理器执行时，使得所述电子设备执行如下步骤：
    利用所述第i个第一图像的所述配准参数，对所述第i个第一图像进行图像配准，得到配准后的第i个第一图像；其中，所述配准后的第i个第一图像用作第i+1个第一图像的基准图像。
  10. 根据权利要求8或9所述的电子设备，其特征在于，所述图像采集装置包括TOF镜头，当所述一个或者多个程序被所述处理器执行时，使得所述电子设备执行如下步骤：
    获取所述第一特征点的第一三维坐标信息和所述第二特征点的第二三维坐标信息；其中，所述第一三维坐标信息和所述第二三维坐标信息均为根据所述TOF镜头的采集数据进行计算所得到的；
    根据所述第一三维坐标信息和所述第二三维坐标信息，对所述第一特征点和所述第二特征点进行匹配，确定所述匹配的特征点对。
  11. 根据权利要求8-10任一项所述的电子设备，其特征在于，所述电子设备中配置有惯性传感器，当所述一个或者多个程序被所述处理器执行时，使得所述电子设备执行如下步骤：
    根据所述图像采集装置采集所述第j个第一图像时、所述惯性传感器采集的传感器数据，对所述第j个第一图像进行图像优化，得到优化后的第j个第一图像；
    根据所述图像采集装置采集所述第i个第一图像时、所述惯性传感器采集的传感器数据，对所述第i个第一图像进行图像优化，得到优化后的第i个第一图像。
  12. 根据权利要求7-11任一项所述的电子设备，其特征在于，当所述一个或者多个程序被所述处理器执行时，使得所述电子设备执行如下步骤：
    基于所述第二图像，采用预设算法对目标图像进行去雾处理，得到去雾后的目标图像；其中，所述目标图像为所述第i个第一图像，或者所述目标图像为对所述n个第一图像进行图像配准后生成的拼接图像。
  13. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储了计算机程序，当所述计算机程序被处理器执行时，使得所述处理器执行权利要求1至6中任一项所述的方法。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115437601A (zh) * 2022-11-02 2022-12-06 荣耀终端有限公司 图像排序方法、电子设备、程序产品及介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107798668A (zh) * 2017-11-23 2018-03-13 北京依锐思遥感技术有限公司 基于rgb影像的无人机成像高光谱几何校正的方法及系统
US20180089804A1 (en) * 2016-09-23 2018-03-29 Purdue Research Foundation Method of processing an image
CN110197504A (zh) * 2019-06-05 2019-09-03 首都师范大学 图像配准方法、装置、电子设备及计算机可读存储介质
WO2020079432A1 (en) * 2018-10-19 2020-04-23 Cancer Research Technology Limited Apparatus and method for wide-field hyperspectral imaging
CN112017221A (zh) * 2020-08-27 2020-12-01 北京理工大学 基于尺度空间的多模态图像配准方法、装置和设备

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10113910B2 (en) * 2014-08-26 2018-10-30 Digimarc Corporation Sensor-synchronized spectrally-structured-light imaging
CN106840398B (zh) * 2017-01-12 2018-02-02 南京大学 一种多光谱光场成像方法
CN107194960B (zh) * 2017-05-22 2019-04-09 中国农业科学院农业资源与农业区划研究所 一种用于高光谱图像的配准方法
CN112233064B (zh) * 2018-07-16 2022-05-20 荣耀终端有限公司 一种色素检测方法及电子设备
CN112532869B (zh) * 2018-10-15 2021-11-19 华为技术有限公司 一种拍摄场景下的图像显示方法及电子设备
CN111327814A (zh) * 2018-12-17 2020-06-23 华为技术有限公司 一种图像处理的方法及电子设备
CN111415388B (zh) * 2020-03-17 2023-10-24 Oppo广东移动通信有限公司 一种视觉定位方法及终端

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180089804A1 (en) * 2016-09-23 2018-03-29 Purdue Research Foundation Method of processing an image
CN107798668A (zh) * 2017-11-23 2018-03-13 北京依锐思遥感技术有限公司 基于rgb影像的无人机成像高光谱几何校正的方法及系统
WO2020079432A1 (en) * 2018-10-19 2020-04-23 Cancer Research Technology Limited Apparatus and method for wide-field hyperspectral imaging
CN110197504A (zh) * 2019-06-05 2019-09-03 首都师范大学 图像配准方法、装置、电子设备及计算机可读存储介质
CN112017221A (zh) * 2020-08-27 2020-12-01 北京理工大学 基于尺度空间的多模态图像配准方法、装置和设备

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115437601A (zh) * 2022-11-02 2022-12-06 荣耀终端有限公司 图像排序方法、电子设备、程序产品及介质
CN115437601B (zh) * 2022-11-02 2024-04-19 荣耀终端有限公司 图像排序方法、电子设备、程序产品及介质

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