CN113393398A - Image noise reduction processing method and device and computer readable storage medium - Google Patents
Image noise reduction processing method and device and computer readable storage medium Download PDFInfo
- Publication number
- CN113393398A CN113393398A CN202110685684.3A CN202110685684A CN113393398A CN 113393398 A CN113393398 A CN 113393398A CN 202110685684 A CN202110685684 A CN 202110685684A CN 113393398 A CN113393398 A CN 113393398A
- Authority
- CN
- China
- Prior art keywords
- image
- frequency domain
- blocks
- noise reduction
- processing method
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000009467 reduction Effects 0.000 title claims abstract description 45
- 238000003672 processing method Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 claims abstract description 52
- 230000004927 fusion Effects 0.000 claims abstract description 46
- 238000001914 filtration Methods 0.000 claims abstract description 23
- 230000009466 transformation Effects 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 20
- 238000003384 imaging method Methods 0.000 claims abstract description 18
- 230000000903 blocking effect Effects 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 6
- 230000035945 sensitivity Effects 0.000 claims description 6
- 230000001131 transforming effect Effects 0.000 claims description 6
- 230000008569 process Effects 0.000 abstract description 17
- 230000000694 effects Effects 0.000 abstract description 14
- 238000000638 solvent extraction Methods 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 17
- 238000004891 communication Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 7
- 238000010295 mobile communication Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000005236 sound signal Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010079 rubber tapping Methods 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001629 suppression Effects 0.000 description 1
- 238000010897 surface acoustic wave method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G06T5/70—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
Abstract
The invention discloses an image noise reduction processing method, equipment and a computer readable storage medium, wherein the method comprises the following steps: partitioning the initial image according to a cutting scale, and setting an overlapping area among a plurality of partitioned image blocks; performing frequency domain transformation on the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining fusion weights of the plurality of frequency domain image blocks during fusion according to the difference between the blocks; carrying out frequency domain filtering and inverse frequency domain transformation on the first image obtained by fusion to obtain a second image; and carrying out slice combination on the second image according to the overlapping state of the overlapping area to obtain an output third image. The method and the device realize an efficient image denoising processing scheme, ensure the clear and real imaging effect of the image and enhance the shooting experience of the user in the process of reducing the image noise.
Description
Technical Field
The present invention relates to the field of mobile communications, and in particular, to a method and an apparatus for image denoising processing, and a computer-readable storage medium.
Background
In the prior art, along with the continuous development of intelligent terminal equipment, the shooting demand of a user on the equipment is higher and higher. In particular, images captured by photographing hardware of a device such as a mobile terminal are generally noisy, and for example, photographing in a night view mode often requires a long shutter time, resulting in more serious noise.
At present, a filtering method is generally adopted to perform noise reduction, but the noise reduction method often causes image blurring. Therefore, an image processing scheme with better noise reduction effect is needed.
Disclosure of Invention
In order to solve the technical defects in the prior art, the invention provides an image noise reduction processing method, which comprises the following steps:
obtaining the cutting scale of the initial image according to the regional characteristics of the initial image, blocking the initial image according to the cutting scale, and setting the overlapping region among the blocked image blocks.
And performing frequency domain transformation on the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining fusion weights of the plurality of frequency domain image blocks during fusion according to the difference between the blocks.
And carrying out frequency domain filtering and inverse frequency domain transformation on the first image obtained by fusion to obtain a second image.
And carrying out slice combination on the second image according to the overlapping state of the overlapping area to obtain an output third image.
Optionally, before the obtaining a cropping scale of the initial image according to a regional feature of the initial image, blocking the initial image by the cropping scale, and setting an overlapping region between a plurality of blocked image blocks, the method includes:
and acquiring a multi-frame image sequence shot under the same exposure parameters.
And selecting a preset frame image from the multi-frame image sequence as a reference frame.
Optionally, before the obtaining a cropping scale of the initial image according to a regional feature of the initial image, blocking the initial image according to the cropping scale, and setting an overlapping region between a plurality of blocked image blocks, the method further includes:
and acquiring the gray level image of each frame of image in the multi-frame image sequence to obtain a gray level image sequence corresponding to the multi-frame image sequence.
And registering the multi-frame image sequence through the gray image sequence and the gray image corresponding to the reference frame to obtain a registered image sequence.
Optionally, the obtaining a cropping scale of the initial image according to a regional feature of the initial image, blocking the initial image according to the cropping scale, and setting an overlapping region between multiple blocked image blocks includes:
and determining the cutting scale according to one or more of sensitivity parameters, shooting environment light parameters, imaging brightness, noise degree and region information in the exposure parameters.
And setting an overlapping proportion corresponding to the cutting scale, and determining the overlapping area among the plurality of blocked image blocks according to the overlapping proportion.
Optionally, the frequency domain transforming the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining the fusion weight of the plurality of frequency domain image blocks during fusion according to the inter-block difference includes:
and performing discrete cosine transform or discrete Fourier transform on each image block of each frame in the registered image sequence to obtain a plurality of frequency domain image blocks.
And acquiring a reference frame image block corresponding to the reference frame.
Optionally, the frequency domain transforming the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining a fusion weight of the plurality of frequency domain image blocks when fusing according to the inter-block difference, further includes:
and acquiring a difference image of the plurality of frequency domain image blocks to be fused and the reference frame image block in each preset color value channel.
Determining fusion weights of the frequency domain image blocks to be fused according to the difference image, and fusing the frequency domain image blocks according to the fusion weights to obtain the first image.
Optionally, the frequency-domain filtering and inverse frequency-domain transforming the fused first image to obtain a second image includes:
-subjecting said first image to an ideal low-pass filtering or a Butterworth filtering.
And performing inverse frequency domain transformation on the first image subjected to the spatial noise reduction to obtain the second image.
Optionally, the slice merging the second image according to the overlapping state of the overlapping region to obtain an output third image includes:
initializing a merge weight value for the slice of each of the overlapping regions.
And traversing each slice, and carrying out weighted summation, slice combination and the like according to the overlapping state between the slices and the combination weight value to obtain the output third image.
The invention also proposes an image noise reduction processing device comprising a memory, a processor and a computer program stored on said memory and executable on said processor, said computer program, when executed by said processor, implementing the steps of the image noise reduction processing method as defined in any one of the above.
The present invention also proposes a computer readable storage medium having stored thereon an image noise reduction processing program which, when executed by a processor, implements the steps of the image noise reduction processing method as defined in any one of the above.
The image denoising processing method, the device and the computer readable storage medium of the invention are implemented, the cutting scale of the initial image is obtained through the regional characteristics of the initial image, the initial image is blocked according to the cutting scale, and the overlapping region among a plurality of blocked image blocks is set; performing frequency domain transformation on the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining fusion weights of the plurality of frequency domain image blocks during fusion according to the difference between the blocks; carrying out frequency domain filtering and inverse frequency domain transformation on the first image obtained by fusion to obtain a second image; and carrying out slice combination on the second image according to the overlapping state of the overlapping area to obtain an output third image. The method and the device realize an efficient image denoising processing scheme, ensure the clear and real imaging effect of the image and enhance the shooting experience of the user in the process of reducing the image noise.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic diagram of a hardware structure of a mobile terminal according to the present invention;
fig. 2 is a communication network system architecture diagram provided by an embodiment of the present invention;
FIG. 3 is a flow chart of a first embodiment of the image denoising processing method according to the present invention;
FIG. 4 is a flow chart of a second embodiment of the image denoising processing method according to the present invention;
FIG. 5 is a flow chart of a third embodiment of the image denoising processing method according to the present invention;
FIG. 6 is a flow chart of a fourth embodiment of the image denoising processing method according to the present invention;
FIG. 7 is a flow chart of a fifth embodiment of the image denoising processing method according to the present invention;
FIG. 8 is a flow chart of a sixth embodiment of the image denoising processing method according to the present invention;
FIG. 9 is a flow chart of a seventh embodiment of the image denoising processing method according to the present invention;
FIG. 10 is a flow chart of an eighth embodiment of the image denoising processing method according to the present invention;
FIG. 11 is a block diagram of an image according to a fourth embodiment of the image denoising method of the present invention;
fig. 12 is a schematic view of merging slices in an eighth embodiment of the image denoising processing method according to the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The terminal may be implemented in various forms. For example, the terminal described in the present invention may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a wearable device, a smart band, a pedometer, and the like, and a fixed terminal such as a Digital TV, a desktop computer, and the like.
The following description will be given by way of example of a mobile terminal, and it will be understood by those skilled in the art that the construction according to the embodiment of the present invention can be applied to a fixed type terminal, in addition to elements particularly used for mobile purposes.
Referring to fig. 1, which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present invention, the mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, a/V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111. Those skilled in the art will appreciate that the mobile terminal architecture shown in fig. 1 is not intended to be limiting of mobile terminals, which may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
The following describes each component of the mobile terminal in detail with reference to fig. 1:
the radio frequency unit 101 may be configured to receive and transmit signals during information transmission and reception or during a call, and specifically, receive downlink information of a base station and then process the downlink information to the processor 110; in addition, the uplink data is transmitted to the base station. Typically, radio frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 101 can also communicate with a network and other devices through wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to GSM (Global System for Mobile communications), GPRS (General Packet Radio Service), CDMA2000(Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access), FDD-LTE (Frequency Division duplex Long Term Evolution), and TDD-LTE (Time Division duplex Long Term Evolution).
WiFi belongs to short-distance wireless transmission technology, and the mobile terminal can help a user to receive and send e-mails, browse webpages, access streaming media and the like through the WiFi module 102, and provides wireless broadband internet access for the user. Although fig. 1 shows the WiFi module 102, it is understood that it does not belong to the essential constitution of the mobile terminal, and may be omitted entirely as needed within the scope not changing the essence of the invention.
The audio output unit 103 may convert audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and output as sound when the mobile terminal 100 is in a call signal reception mode, a call mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. Also, the audio output unit 103 may also provide audio output related to a specific function performed by the mobile terminal 100 (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 may include a speaker, a buzzer, and the like.
The a/V input unit 104 is used to receive audio or video signals. The a/V input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, the Graphics processor 1041 Processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphic processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 may receive sounds (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, or the like, and may be capable of processing such sounds into audio data. The processed audio (voice) data may be converted into a format output transmittable to a mobile communication base station via the radio frequency unit 101 in case of a phone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.
The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor includes an ambient light sensor that can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 1061 and/or a backlight when the mobile terminal 100 is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications of recognizing the posture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer and tapping), and the like; as for other sensors such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the mobile phone, further description is omitted here.
The display unit 106 is used to display information input by a user or information provided to the user. The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like.
The user input unit 107 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect a touch operation performed by a user on or near the touch panel 1071 (e.g., an operation performed by the user on or near the touch panel 1071 using a finger, a stylus, or any other suitable object or accessory), and drive a corresponding connection device according to a predetermined program. The touch panel 1071 may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. In addition to the touch panel 1071, the user input unit 107 may include other input devices 1072. In particular, other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.
Further, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the processor 110 to determine the type of the touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although the touch panel 1071 and the display panel 1061 are shown in fig. 1 as two separate components to implement the input and output functions of the mobile terminal, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions of the mobile terminal, and is not limited herein.
The interface unit 108 serves as an interface through which at least one external device is connected to the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input (e.g., data information, power, etc.) from external devices and transmit the received input to one or more elements within the mobile terminal 100 or may be used to transmit data between the mobile terminal 100 and external devices.
The memory 109 may be used to store software programs as well as various data. The memory 109 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 109 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, or other volatile solid state storage device.
The processor 110 is a control center of the mobile terminal, connects various parts of the entire mobile terminal using various interfaces and lines, and performs various functions of the mobile terminal and processes data by operating or executing software programs and/or modules stored in the memory 109 and calling data stored in the memory 109, thereby performing overall monitoring of the mobile terminal. Processor 110 may include one or more processing units; preferably, the processor 110 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.
The mobile terminal 100 may further include a power supply 111 (e.g., a battery) for supplying power to various components, and preferably, the power supply 111 may be logically connected to the processor 110 via a power management system, so as to manage charging, discharging, and power consumption management functions via the power management system.
Although not shown in fig. 1, the mobile terminal 100 may further include a bluetooth module or the like, which is not described in detail herein.
In order to facilitate understanding of the embodiments of the present invention, a communication network system on which the mobile terminal of the present invention is based is described below.
Referring to fig. 2, fig. 2 is an architecture diagram of a communication Network system according to an embodiment of the present invention, where the communication Network system is an LTE system of a universal mobile telecommunications technology, and the LTE system includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203, and an IP service 204 of an operator, which are in communication connection in sequence.
Specifically, the UE201 may be the terminal 100 described above, and is not described herein again.
The E-UTRAN202 includes eNodeB2021 and other eNodeBs 2022, among others. Among them, the eNodeB2021 may be connected with other eNodeB2022 through backhaul (e.g., X2 interface), the eNodeB2021 is connected to the EPC203, and the eNodeB2021 may provide the UE201 access to the EPC 203.
The EPC203 may include an MME (Mobility Management Entity) 2031, an HSS (Home Subscriber Server) 2032, other MMEs 2033, an SGW (Serving gateway) 2034, a PGW (PDN gateway) 2035, and a PCRF (Policy and Charging Rules Function) 2036, and the like. The MME2031 is a control node that handles signaling between the UE201 and the EPC203, and provides bearer and connection management. HSS2032 is used to provide registers to manage functions such as home location register (not shown) and holds subscriber specific information about service characteristics, data rates, etc. All user data may be sent through SGW2034, PGW2035 may provide IP address assignment for UE201 and other functions, and PCRF2036 is a policy and charging control policy decision point for traffic data flow and IP bearer resources, which selects and provides available policy and charging control decisions for a policy and charging enforcement function (not shown).
The IP services 204 may include the internet, intranets, IMS (IP Multimedia Subsystem), or other IP services, among others.
Although the LTE system is described as an example, it should be understood by those skilled in the art that the present invention is not limited to the LTE system, but may also be applied to other wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems.
Based on the above mobile terminal hardware structure and communication network system, the present invention provides various embodiments of the method.
Example one
Fig. 3 is a flowchart of a first embodiment of the image noise reduction processing method of the present invention. An image noise reduction processing method, the method comprising:
s1, obtaining the cutting scale of the initial image according to the regional characteristics of the initial image, blocking the initial image according to the cutting scale, and setting the overlapping region among the blocked image blocks.
And S2, performing frequency domain transformation on the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining fusion weights of the plurality of frequency domain image blocks during fusion according to the inter-block difference.
And S3, performing frequency domain filtering and inverse frequency domain transformation on the fused first image to obtain a second image.
And S4, carrying out slice combination on the second image according to the overlapping state of the overlapping area to obtain an output third image.
Optionally, in this embodiment, a cropping scale of an initial image is obtained according to a region feature of the initial image, the initial image is segmented by the cropping scale, and an overlapping region between a plurality of segmented image blocks is set. In the embodiment, the image is divided into blocks and each image block is subjected to noise reduction processing in consideration of the image area characteristics.
Optionally, in this embodiment, when the image is partitioned, an overlapping region is set between blocks, so as to avoid a blocking effect generated after the fusion.
Optionally, in this embodiment, frequency domain transformation is performed on the plurality of image blocks to obtain a plurality of frequency domain image blocks, and a fusion weight of the plurality of frequency domain image blocks during fusion is determined according to an inter-block difference. In the fusion, the fusion weight is determined by the difference between blocks to avoid the problems of ghost artifacts and the like caused by poor alignment.
Optionally, in this embodiment, frequency domain filtering and inverse frequency domain transformation are performed on the first image obtained by fusion to obtain a second image. In the embodiment, spatial noise reduction is considered while multi-frame fusion is performed, so that a better noise reduction effect is obtained.
Optionally, in this embodiment, the second image is sliced and merged according to the overlapping state of the overlapping region to obtain an output third image, so that through multi-frame noise reduction, image noise is reduced, the image is clear and real, and a good imaging effect is obtained.
The method has the advantages that the cutting scale of the initial image is obtained through the regional characteristics of the initial image, the initial image is partitioned according to the cutting scale, and the overlapped region of the partitioned image blocks is set; performing frequency domain transformation on the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining fusion weights of the plurality of frequency domain image blocks during fusion according to the difference between the blocks; carrying out frequency domain filtering and inverse frequency domain transformation on the first image obtained by fusion to obtain a second image; and carrying out slice combination on the second image according to the overlapping state of the overlapping area to obtain an output third image. The method and the device realize an efficient image denoising processing scheme, ensure the clear and real imaging effect of the image and enhance the shooting experience of the user in the process of reducing the image noise.
Example two
Fig. 4 is a flowchart of a second embodiment of an image denoising processing method according to the present invention, where based on the above embodiments, before obtaining a cropping scale of an initial image according to a region feature of the initial image, partitioning the initial image according to the cropping scale, and setting an overlapping region between multiple blocked image blocks, the method includes:
and S01, acquiring the multi-frame image sequence shot under the same exposure parameters.
And S02, selecting a preset frame image in the multi-frame image sequence as a reference frame.
Alternatively, in the present embodiment, a multi-frame image sequence I ═ { I ═ is photographed under the same exposure parameters1,I2,……,In}。
Optionally, in this embodiment, a frame of image (for example, the 0 th frame in the multi-frame image sequence) is selected as the reference frame I in the multi-frame image sequence Iref。
The method has the advantages that the multi-frame image sequence obtained by shooting under the same exposure parameters is obtained; and selecting a preset frame image from the multi-frame image sequence as a reference frame. The image frame data is provided for realizing an efficient image noise reduction processing scheme, the clear and real imaging effect of the image is ensured simultaneously in the process of reducing the image noise, and the shooting experience of a user is enhanced.
EXAMPLE III
Fig. 5 is a flowchart of an image denoising processing method according to a third embodiment of the present invention, where based on the above embodiments, before obtaining a cropping scale of an initial image according to a region feature of the initial image, partitioning the initial image according to the cropping scale, and setting an overlapping region between multiple blocked image blocks, the method further includes:
and S03, acquiring the gray level image of each frame image in the multi-frame image sequence to obtain the gray level image sequence corresponding to the multi-frame image sequence.
And S04, registering the multi-frame image sequence through the gray image sequence and the gray image corresponding to the reference frame to obtain a registered image sequence.
Optionally, in this embodiment, a grayscale image of each frame of image in the multi-frame image sequence I is obtained to form a grayscale image sequence corresponding to the input multi-frame image sequence, where G ═ { gray ═ gray }1,gray2,……,grayn}; wherein, the reference image IrefGray scale image ofref。
Alternatively, in the present embodiment, the gray-scale image sequence G of the input image sequence and the gray-scale image gray of the selected reference frame are usedrefRegistering the input image sequence to obtain a registered image sequence I′＝{I′ 1,I′ 2,……,I′ n}。
The method has the advantages that the gray level image sequence corresponding to the multi-frame image sequence is obtained by obtaining the gray level image of each frame of image in the multi-frame image sequence; and registering the multi-frame image sequence through the gray image sequence and the gray image corresponding to the reference frame to obtain a registered image sequence. The registered image frame sequence is provided for realizing an efficient image noise reduction processing scheme, the clear and real imaging effect of the image is ensured simultaneously in the process of reducing the image noise, and the shooting experience of a user is enhanced.
Example four
Fig. 6 is a flowchart of a fourth embodiment of an image denoising processing method according to the present invention, where based on the above embodiments, the obtaining a cropping scale of an initial image according to a region feature of the initial image, partitioning the initial image according to the cropping scale, and setting an overlapping region between multiple blocked image blocks includes:
and S11, determining the cropping scale according to one or more of sensitivity parameters, shooting environment light parameters, imaging brightness, noise degree and region information in the exposure parameters.
S12, setting an overlapping proportion corresponding to the clipping scale, and determining the overlapping area among the plurality of blocked image blocks according to the overlapping proportion.
Optionally, in this embodiment, the image is cropped using an adaptive slice scale.
Optionally, in this embodiment, an adaptive block size is used when an image is sliced in consideration of different imaging characteristics in different illumination environments, so as to avoid blurring caused by spatial noise reduction.
Alternatively, in the present embodiment, the cropping scale is determined according to one or more of a sensitivity parameter, a shooting environment light parameter, imaging brightness, a noise level, and area information in the exposure parameters, for example, the greater the sensitivity (ISO) parameter, the darker the ambient light when shooting is indicated, the darker the imaging is, the more the noise is obvious, and the less the area information is, the larger the slice size is required.
Optionally, in this embodiment, first, an ISO parameter in the exposure parameters is obtained, and then, the clipping scale block _ size is obtained according to the ISO parameter. The larger ISO is, the larger block _ size is, for example, 32 for ISO 1000 and 64 for ISO 2000; finally, the image is cut into image blocks according to block _ size, with a given range of overlap preserved between the image blocks, e.g. settings
The overlap ratio of (a). Specifically, referring to the image block diagram shown in fig. 11, one frame of image is divided into block 1, block 2, block 3, block 4, block 5, and block 6, where there is an overlap between block 1, block 2, block 3, block 4, block 5, and block 6.
The method has the advantages that the cropping scale is determined according to one or more of sensitivity parameters, shooting environment light parameters, imaging brightness, noise degree and region information in the exposure parameters; and setting an overlapping proportion corresponding to the cutting scale, and determining the overlapping area among the plurality of blocked image blocks according to the overlapping proportion. The overlapping and cutting modes are provided for realizing an efficient image noise reduction processing scheme, the clear and real imaging effect of the image is ensured simultaneously in the process of reducing the image noise, and the shooting experience of a user is enhanced.
EXAMPLE five
Fig. 7 is a flowchart of a fifth embodiment of the image denoising processing method according to the present invention, where based on the above embodiments, the frequency domain transforming the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining the fusion weight of the plurality of frequency domain image blocks during fusion according to the inter-block difference includes:
and S21, performing discrete cosine transform or discrete Fourier transform on each image block of each frame in the registered image sequence to obtain a plurality of frequency domain image blocks.
And S22, acquiring the reference frame image block corresponding to the reference frame.
Optionally, in this embodiment, the frequency domain transform includes a discrete cosine transform and a discrete fourier transform.
Optionally, in this embodiment, each image block of each frame in the registered image sequence is subjected to one or more of discrete cosine transform and discrete fourier transform, so as to obtain a plurality of frequency domain image blocks.
The embodiment has the advantages that a plurality of frequency domain image blocks are obtained by performing discrete cosine transform or discrete fourier transform on each image block of each frame in the registered image sequence; and acquiring a reference frame image block corresponding to the reference frame. A frequency domain transformation mode is provided for realizing an efficient image noise reduction processing scheme, the clear and real imaging effect of the image is ensured simultaneously in the process of reducing the image noise, and the shooting experience of a user is enhanced.
EXAMPLE six
Fig. 8 is a flowchart of a sixth embodiment of the image denoising processing method according to the present invention, where based on the above embodiments, the method performs frequency domain transformation on a plurality of image blocks to obtain a plurality of frequency domain image blocks, and determines fusion weights of the plurality of frequency domain image blocks during fusion according to inter-block differences, and further includes:
and S23, obtaining a difference image of the frequency domain image blocks to be fused and the reference frame image blocks in each preset color value channel.
And S24, determining the fusion weight of the frequency domain image blocks to be fused according to the difference image, and fusing the frequency domain image blocks according to the fusion weight to obtain the first image.
Optionally, in this embodiment, multi-frame fusion is performed on corresponding image blocks in a multi-frame sequence, and a wiener filter is used to control a contribution weight value of a frame to be fused in the fusion.
Alternatively, in this embodiment, when the registration effect is not good, a smaller difference map (in D) will be obtainedz(w) to avoid artifacts, ghosts, etc. generated during the fusion process.
Optionally, in this embodiment, a difference map of each frame tile to be fused and the reference frame tile in each channel (for example, R, G, B channels) is calculated: dz(w) wherein:
Dz(w)＝T0(w)-Tz(w)。
optionally, in this embodiment, the fusion weight of each image block to be fused is calculated: a. thez(w) wherein:
optionally, in this embodiment, frequency domain fusion is performed on each frame of image block to obtain a fused image block, where:
the method has the advantages that a difference image of each preset color value channel of a plurality of frequency domain image blocks to be fused and the reference frame image block is obtained; determining fusion weights of the frequency domain image blocks to be fused according to the difference image, and fusing the frequency domain image blocks according to the fusion weights to obtain the first image. The method provides a multi-frame fusion mode for realizing an efficient image noise reduction processing scheme, ensures the imaging effect of clear images and real images in the process of reducing image noise, and enhances the shooting experience of users.
EXAMPLE seven
Fig. 9 is a flowchart of a seventh embodiment of the image denoising processing method according to the present invention, where based on the above embodiments, the performing frequency domain filtering and inverse frequency domain transformation on the fused first image to obtain a second image includes:
s31, performing ideal low-pass filtering or Butterworth filtering on the first image.
And S32, performing inverse frequency domain transformation on the first image subjected to the spatial noise reduction to obtain the second image.
Optionally, in this embodiment, the fused tiles are spatially denoised.
Optionally, in this embodiment, the spatial noise reduction is performed by performing frequency-domain filtering on the frequency-domain image blocks, where the frequency-domain filtering includes one or both of ideal low-pass filtering and butterworth filtering.
The present embodiment has the beneficial effects that, by performing ideal low-pass filtering or butterworth filtering on the first image; and performing inverse frequency domain transformation on the first image subjected to the spatial noise reduction to obtain the second image. The method provides a space noise reduction mode for realizing an efficient image noise reduction processing scheme, ensures the imaging effect of clear images and real images in the process of reducing image noise, and enhances the shooting experience of users.
Example eight
Fig. 10 is a flowchart of an eighth embodiment of the image denoising processing method according to the present invention, and based on the above embodiment, the slicing and merging the second image according to the overlapping state of the overlapping region to obtain an output third image includes:
and S41, initializing the merging weight value of the slice of each overlapping area.
And S42, traversing each slice, and performing weighted summation slice combination according to the overlapping state between each slice and the combination weight value to obtain the output third image.
Optionally, in this embodiment, first, a slice merge weight value of each overlapped region is initialized, for example, there is no overlap in some regions, and there is no merge weight.
Optionally, in this embodiment, the partial region is from two slice overlaps, and initialized using a trigonometric function:
θ＝π*((i+0.5)/(block_size))；
weight0[i]＝0.5*(cos(θ)+1)；
weight1[i]＝weight0[i]。
optionally, in this embodiment, the partial region is from four slice overlaps, and initialized using a trigonometric function:
weight00[i,j]＝weight0[i]*weight0[j]；
weight01[i,j]＝weight0[i]*weight1[j]；
weight10[i,j]＝weight1[i]*weight0[j]；
weight11[i,j]＝1-weight00[i,j]-weight01[i,j]-weight10[i,j]。
optionally, in this embodiment, please refer to the slice merging diagram shown in fig. 12, in this embodiment, the slices are traversed, and the slices are weighted and merged according to the overlapping condition of the slices.
The embodiment has the advantages that the merging weight values of the slices of the overlapped areas are initialized; and traversing each slice, and carrying out weighted summation, slice combination and the like according to the overlapping state between the slices and the combination weight value to obtain the output third image. The slice merging mode is provided for realizing an efficient image noise reduction processing scheme, the clear and real imaging effect of the image is ensured simultaneously in the process of reducing the image noise, and the shooting experience of a user is enhanced.
Example nine
Based on the above embodiments, the present invention also provides an image noise reduction processing apparatus, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the image noise reduction processing method according to any one of the above.
It should be noted that the device embodiment and the method embodiment belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment, and technical features in the method embodiment are correspondingly applicable in the device embodiment, which is not described herein again.
Example ten
Based on the above embodiments, the present invention also provides a computer readable storage medium, having an image noise reduction processing program stored thereon, where the image noise reduction processing program, when executed by a processor, implements the steps of the image noise reduction processing method according to any one of the above.
It should be noted that the media embodiment and the method embodiment belong to the same concept, and specific implementation processes thereof are detailed in the method embodiment, and technical features in the method embodiment are correspondingly applicable in the media embodiment, which is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. An image noise reduction processing method, characterized by comprising:
acquiring a cutting scale of an initial image according to the regional characteristics of the initial image, blocking the initial image according to the cutting scale, and setting an overlapping region between a plurality of blocked image blocks;
performing frequency domain transformation on the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining fusion weights of the plurality of frequency domain image blocks during fusion according to the difference between the blocks;
carrying out frequency domain filtering and inverse frequency domain transformation on the first image obtained by fusion to obtain a second image;
and carrying out slice combination on the second image according to the overlapping state of the overlapping area to obtain an output third image.
2. The image noise reduction processing method according to claim 1, wherein before the obtaining of the cropping scale of the initial image according to the regional characteristics of the initial image, the blocking of the initial image with the cropping scale, and the setting of the overlapping region between the blocked image blocks, the method comprises:
acquiring a multi-frame image sequence shot under the same exposure parameters;
and selecting a preset frame image from the multi-frame image sequence as a reference frame.
3. The image denoising processing method according to claim 2, wherein before obtaining the cropping scale of the initial image according to the regional characteristics of the initial image, blocking the initial image with the cropping scale, and setting an overlapping region between a plurality of blocked image blocks, the method further comprises:
acquiring a gray level image of each frame of image in the multi-frame image sequence to obtain a gray level image sequence corresponding to the multi-frame image sequence;
and registering the multi-frame image sequence through the gray image sequence and the gray image corresponding to the reference frame to obtain a registered image sequence.
4. The image noise reduction processing method according to claim 3, wherein the obtaining a cropping scale of the initial image according to a regional characteristic of the initial image, blocking the initial image at the cropping scale, and setting an overlapping region between a plurality of blocked image blocks comprises:
determining the cutting scale according to one or more of sensitivity parameters, shooting environment light parameters, imaging brightness, noise degree and region information in the exposure parameters;
and setting an overlapping proportion corresponding to the cutting scale, and determining the overlapping area among the plurality of blocked image blocks according to the overlapping proportion.
5. The image denoising processing method according to claim 4, wherein the frequency domain transforming the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining the fusion weight of the plurality of frequency domain image blocks when fusing according to the inter-block difference comprises:
performing discrete cosine transform or discrete Fourier transform on each image block of each frame in the registered image sequence to obtain a plurality of frequency domain image blocks;
and acquiring a reference frame image block corresponding to the reference frame.
6. The image denoising processing method according to claim 5, wherein the frequency domain transforming the plurality of image blocks to obtain a plurality of frequency domain image blocks, and determining fusion weights for the plurality of frequency domain image blocks according to the inter-block difference, further comprises:
obtaining a difference image of a plurality of frequency domain image blocks to be fused and the reference frame image block in each preset color value channel;
determining fusion weights of the frequency domain image blocks to be fused according to the difference image, and fusing the frequency domain image blocks according to the fusion weights to obtain the first image.
7. The image noise reduction processing method according to claim 6, wherein the performing frequency domain filtering and inverse frequency domain transformation on the fused first image to obtain a second image comprises:
-performing an ideal low-pass filtering or butterworth filtering of said first image;
and performing inverse frequency domain transformation on the first image subjected to the spatial noise reduction to obtain the second image.
8. The image noise reduction processing method according to claim 7, wherein the slice merging the second image according to the overlapping state of the overlapping region to obtain an output third image includes:
initializing a merging weight value of the slices of each overlapping area;
and traversing each slice, and carrying out weighted summation, slice combination and the like according to the overlapping state between the slices and the combination weight value to obtain the output third image.
9. An image noise reduction processing apparatus, characterized in that the apparatus comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the image noise reduction processing method according to any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon an image noise reduction processing program which, when executed by a processor, implements the steps of the image noise reduction processing method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110685684.3A CN113393398A (en) | 2021-06-21 | 2021-06-21 | Image noise reduction processing method and device and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110685684.3A CN113393398A (en) | 2021-06-21 | 2021-06-21 | Image noise reduction processing method and device and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113393398A true CN113393398A (en) | 2021-09-14 |
Family
ID=77623236
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110685684.3A Pending CN113393398A (en) | 2021-06-21 | 2021-06-21 | Image noise reduction processing method and device and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113393398A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113781357A (en) * | 2021-09-24 | 2021-12-10 | Oppo广东移动通信有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| WO2023134103A1 (en) * | 2022-01-14 | 2023-07-20 | 无锡英菲感知技术有限公司 | Image fusion method, device, and storage medium |
-
2021
- 2021-06-21 CN CN202110685684.3A patent/CN113393398A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113781357A (en) * | 2021-09-24 | 2021-12-10 | Oppo广东移动通信有限公司 | Image processing method, image processing device, electronic equipment and storage medium |
| WO2023134103A1 (en) * | 2022-01-14 | 2023-07-20 | 无锡英菲感知技术有限公司 | Image fusion method, device, and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107093418B (en) | Screen display method, computer equipment and storage medium | |
| CN108024065B (en) | Terminal shooting method, terminal and computer readable storage medium | |
| CN108038834B (en) | Method, terminal and computer readable storage medium for reducing noise | |
| CN108459799B (en) | Picture processing method, mobile terminal and computer readable storage medium | |
| CN111654628B (en) | Video shooting method and device and computer readable storage medium | |
| CN108198150B (en) | Method for eliminating image dead pixel, terminal and storage medium | |
| CN110069122B (en) | Screen control method, terminal and computer readable storage medium | |
| CN113179370A (en) | Photographing method, mobile terminal and readable storage medium | |
| CN111885307A (en) | Depth-of-field shooting method and device and computer readable storage medium | |
| CN113393398A (en) | Image noise reduction processing method and device and computer readable storage medium | |
| CN112135053A (en) | Image processing method, mobile terminal and computer readable storage medium | |
| CN109710159B (en) | Flexible screen response method and device and computer readable storage medium | |
| CN113222850A (en) | Image processing method, device and computer readable storage medium | |
| CN112598678A (en) | Image processing method, terminal and computer readable storage medium | |
| CN110223239B (en) | Image processing method, terminal and computer readable storage medium | |
| CN112135045A (en) | Video processing method, mobile terminal and computer storage medium | |
| CN107743204B (en) | Exposure processing method, terminal, and computer-readable storage medium | |
| CN112532838B (en) | Image processing method, mobile terminal and computer storage medium | |
| CN113473029A (en) | Lightning image shooting method and device and computer readable storage medium | |
| CN114040073A (en) | Starry sky image shooting processing method and equipment and computer readable storage medium | |
| CN108335301B (en) | Photographing method and mobile terminal | |
| CN109215004B (en) | Image synthesis method, mobile terminal and computer readable storage medium | |
| CN108259765B (en) | Shooting method, terminal and computer readable storage medium | |
| CN112135047A (en) | Image processing method, mobile terminal and computer storage medium | |
| CN107959793B (en) | Image processing method, terminal and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |