CN116468883A - High-precision image data volume fog recognition method and device - Google Patents
High-precision image data volume fog recognition method and device Download PDFInfo
- Publication number
- CN116468883A CN116468883A CN202310317281.2A CN202310317281A CN116468883A CN 116468883 A CN116468883 A CN 116468883A CN 202310317281 A CN202310317281 A CN 202310317281A CN 116468883 A CN116468883 A CN 116468883A
- Authority
- CN
- China
- Prior art keywords
- image data
- volume
- fog
- original image
- information
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000007781 pre-processing Methods 0.000 claims abstract description 38
- 238000012545 processing Methods 0.000 claims description 52
- 238000000605 extraction Methods 0.000 claims description 23
- 238000000354 decomposition reaction Methods 0.000 claims description 10
- 230000010354 integration Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 6
- 230000008878 coupling Effects 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 4
- 238000005859 coupling reaction Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 230000004069 differentiation Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003780 insertion Methods 0.000 description 1
- 230000037431 insertion Effects 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
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a high-precision image data volume fog recognition method and device. Wherein the method comprises the following steps: acquiring volume information and pixel information of original image data; preprocessing the original image data according to the range of the volume information to obtain first image data; integrating the first image data and the pixel information to obtain second image data; and inputting the second image data into a volumetric fog algorithm model to obtain an image volumetric fog identification result. The invention solves the technical problems that the identification method of the volume fog in the image data in the prior art only uses the characteristic quantity of the volume fog algorithm to substitute and calculate the original image data to obtain the area related to the volume fog for output, and the parameters of the volume fog can not be determined according to the range of the volume fog and the data condition of a specific image.
Description
Technical Field
The invention relates to the field of image data identification, in particular to a high-precision image data volume fog identification method and device.
Background
Along with the continuous development of intelligent science and technology, intelligent equipment is increasingly used in life, work and study of people, and the quality of life of people is improved and the learning and working efficiency of people is increased by using intelligent science and technology means.
Currently, for an image acquired by a high-precision image capturing apparatus, in order to increase the processing capability of the image, it is generally required to identify a volumetric fog region in the image, and perform volumetric fog processing according to an accurate identification result, so as to strengthen or weaken the image display effect. However, the method for identifying the volume fog in the image data in the prior art only uses the characteristic quantity of the volume fog algorithm to substitute and calculate the original image data, so that the area related to the volume fog is obtained and output, and the parameters of the volume fog cannot be determined according to the range of the volume fog and the data condition of a specific image.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a high-precision image data volume fog identification method and device, which at least solve the technical problems that in the identification method of volume fog in image data in the prior art, original image data is substituted and calculated by utilizing volume fog algorithm feature quantity, a region related to the volume fog is obtained and output, and parameters of the volume fog cannot be determined according to the range of the volume fog and the data condition of a specific image.
According to an aspect of an embodiment of the present invention, there is provided a high-precision image data volume fog recognition method including: acquiring volume information and pixel information of original image data; preprocessing the original image data according to the range of the volume information to obtain first image data; integrating the first image data and the pixel information to obtain second image data; and inputting the second image data into a volumetric fog algorithm model to obtain an image volumetric fog identification result.
Optionally, the acquiring the volume information and the pixel information of the original image data includes: differentiating the volume coordinates of the original image data to obtain the volume information; and carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
Optionally, preprocessing the raw image data according to the range of the volume information to obtain first image data includes: selecting a range of volume information with the occurrence possibility of volume fog according to the original image data; determining a set of image data to be processed in the original image data according to the range of the volume information; performing the preprocessing on the set of image data to be processed to obtain the first image data, wherein the preprocessing comprises: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed.
Optionally, the volumetric fog algorithm model includes:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
According to another aspect of the embodiment of the present invention, there is also provided a high-precision image data volume fog recognition apparatus, including: the acquisition module is used for acquiring volume information and pixel information of the original image data; the preprocessing module is used for preprocessing the original image data according to the range of the volume information to obtain first image data; the integration module is used for integrating the first image data and the pixel information to obtain second image data; and the input module is used for inputting the second image data into the volumetric fog algorithm model to obtain an image volumetric fog identification result.
Optionally, the acquiring module includes: the differentiating unit is used for differentiating the volume coordinates of the original image data to obtain the volume information; and the extraction unit is used for carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
Optionally, the preprocessing module includes: a selection unit configured to select a range of volume information having a possibility of occurrence of volume fog from the original image data; a determining unit for determining a set of image data to be processed in the original image data according to the range of the volume information; a processing unit, configured to perform the preprocessing on the set of image data to be processed to obtain the first image data, where the preprocessing includes: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed.
Optionally, the volumetric fog algorithm model includes:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
According to another aspect of the embodiment of the present invention, there is also provided a nonvolatile storage medium including a stored program, where the program when executed controls a device in which the nonvolatile storage medium is located to execute a high-precision image data volume fog recognition method.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a high-precision image data volumetric fog recognition method when executed.
In the embodiment of the invention, volume information and pixel information of original image data are acquired; preprocessing the original image data according to the range of the volume information to obtain first image data; integrating the first image data and the pixel information to obtain second image data; the second image data is input into the volumetric fog algorithm model to obtain an image volumetric fog recognition result, so that the technical problem that parameters of the volumetric fog cannot be determined according to the range of the volumetric fog and the data condition of a specific image because the original image data is substituted and calculated by the volumetric fog algorithm feature quantity to obtain the region related to the volumetric fog to be output in the volumetric fog recognition method in the image data in the prior art is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a high precision image data volumetric fog identification method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a high-precision image data volume fog recognition device according to an embodiment of the present invention;
fig. 3 is a block diagram of a terminal device for performing the method according to the invention according to an embodiment of the invention;
fig. 4 is a memory unit for holding or carrying program code for implementing a method according to the invention, according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present invention, there is provided a method embodiment of a high precision image data volumetric fog identification method, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer executable instructions, and, although a logical sequence is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in a different order than that illustrated herein.
Example 1
Fig. 1 is a flowchart of a high-precision image data volume fog recognition method according to an embodiment of the present invention, as shown in fig. 1, the method comprising the steps of:
step S102, acquiring volume information and pixel information of original image data.
Specifically, in order to solve the technical problem that in the prior art, the method for identifying the volume fog in the image data only uses the characteristic quantity of the volume fog algorithm to substitute and calculate the original image data, the area related to the volume fog is obtained and output, and the parameters of the volume fog cannot be determined according to the range of the volume fog and the data condition of a specific image, the original image data needs to be acquired through the image capturing device when the embodiment of the invention is implemented, and the volume information and the pixel information of the original image data are extracted, so that the image data can be calculated through a volume fog algorithm model later.
Optionally, the acquiring the volume information and the pixel information of the original image data includes: differentiating the volume coordinates of the original image data to obtain the volume information; and carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
Specifically, in order to obtain image volume information through each parameter in original image data, it is necessary to conduct volume coordinate differentiation on the original image data to obtain the volume information, and in order to obtain pixel information through parameters in the original image data, it is necessary to conduct pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
Step S104, preprocessing the original image data according to the range of the volume information to obtain first image data.
Optionally, preprocessing the raw image data according to the range of the volume information to obtain first image data includes: selecting a range of volume information with the occurrence possibility of volume fog according to the original image data; determining a set of image data to be processed in the original image data according to the range of the volume information; performing the preprocessing on the set of image data to be processed to obtain the first image data, wherein the preprocessing comprises: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed.
Specifically, in order to identify the volume fog of the original image data obtained by the embodiment of the present invention, the image having the volume fog range in the original image data needs to be preprocessed, where the preprocessing may be half-binarization processing, that is, processing without full gray level binarization, and the processing may be performed by using 4 different precision level decomposition for display, so as to obtain the first image data.
Step S106, integrating the first image data with the pixel information to obtain second image data.
Specifically, in order to bind all image data in the first image data with corresponding pixel information in the subsequent volumetric fog algorithm model, so that the subsequent processor can conveniently identify the corresponding volumetric fog region position, the first image data and the pixel information need to be integrated to obtain the second image data.
And S108, inputting the second image data into a volumetric fog algorithm model to obtain an image volumetric fog identification result.
Optionally, the volumetric fog algorithm model includes:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
By the embodiment, the technical problems that the identification method of the volume fog in the image data in the prior art only utilizes the characteristic quantity of the volume fog algorithm to substitute and calculate the original image data, the area related to the volume fog is obtained and output, and the parameters of the volume fog cannot be determined according to the range of the volume fog and the data condition of a specific image are solved.
Example two
Fig. 2 is a block diagram of a high-precision image data volume fog recognition apparatus according to an embodiment of the present invention, as shown in fig. 2, the apparatus comprising:
the acquiring module 20 is configured to acquire volume information and pixel information of the original image data.
Specifically, in order to solve the technical problem that in the prior art, the method for identifying the volume fog in the image data only uses the characteristic quantity of the volume fog algorithm to substitute and calculate the original image data, the area related to the volume fog is obtained and output, and the parameters of the volume fog cannot be determined according to the range of the volume fog and the data condition of a specific image, the original image data needs to be acquired through the image capturing device when the embodiment of the invention is implemented, and the volume information and the pixel information of the original image data are extracted, so that the image data can be calculated through a volume fog algorithm model later.
Optionally, the acquiring module includes: the differentiating unit is used for differentiating the volume coordinates of the original image data to obtain the volume information; and the extraction unit is used for carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
Specifically, in order to obtain image volume information through each parameter in original image data, it is necessary to conduct volume coordinate differentiation on the original image data to obtain the volume information, and in order to obtain pixel information through parameters in the original image data, it is necessary to conduct pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
The preprocessing module 22 is configured to preprocess the raw image data according to the range of the volume information, so as to obtain first image data.
Optionally, the preprocessing module includes: a selection unit configured to select a range of volume information having a possibility of occurrence of volume fog from the original image data; a determining unit for determining a set of image data to be processed in the original image data according to the range of the volume information; a processing unit, configured to perform the preprocessing on the set of image data to be processed to obtain the first image data, where the preprocessing includes: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed.
Specifically, in order to identify the volume fog of the original image data obtained by the embodiment of the present invention, the image having the volume fog range in the original image data needs to be preprocessed, where the preprocessing may be half-binarization processing, that is, processing without full gray level binarization, and the processing may be performed by using 4 different precision level decomposition for display, so as to obtain the first image data.
And an integration module 24, configured to integrate the first image data and the pixel information to obtain second image data.
Specifically, in order to bind all image data in the first image data with corresponding pixel information in the subsequent volumetric fog algorithm model, so that the subsequent processor can conveniently identify the corresponding volumetric fog region position, the first image data and the pixel information need to be integrated to obtain the second image data.
And the input module 26 is configured to input the second image data to a volumetric fog algorithm model, so as to obtain an image volumetric fog identification result.
Optionally, the volumetric fog algorithm model includes:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
By the embodiment, the technical problems that the identification method of the volume fog in the image data in the prior art only utilizes the characteristic quantity of the volume fog algorithm to substitute and calculate the original image data, the area related to the volume fog is obtained and output, and the parameters of the volume fog cannot be determined according to the range of the volume fog and the data condition of a specific image are solved.
According to another aspect of the embodiment of the present invention, there is also provided a nonvolatile storage medium including a stored program, where the program when executed controls a device in which the nonvolatile storage medium is located to execute a high-precision image data volume fog recognition method.
Specifically, the method comprises the following steps: acquiring volume information and pixel information of original image data; preprocessing the original image data according to the range of the volume information to obtain first image data; integrating the first image data and the pixel information to obtain second image data; and inputting the second image data into a volumetric fog algorithm model to obtain an image volumetric fog identification result. Optionally, the acquiring the volume information and the pixel information of the original image data includes: differentiating the volume coordinates of the original image data to obtain the volume information; and carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information. Optionally, preprocessing the raw image data according to the range of the volume information to obtain first image data includes: selecting a range of volume information with the occurrence possibility of volume fog according to the original image data; determining a set of image data to be processed in the original image data according to the range of the volume information; performing the preprocessing on the set of image data to be processed to obtain the first image data, wherein the preprocessing comprises: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed. Optionally, the volumetric fog algorithm model includes:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
According to another aspect of the embodiment of the present invention, there is also provided an electronic device including a processor and a memory; the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, where the computer readable instructions execute a high-precision image data volumetric fog recognition method when executed.
Specifically, the method comprises the following steps: acquiring volume information and pixel information of original image data; preprocessing the original image data according to the range of the volume information to obtain first image data; integrating the first image data and the pixel information to obtain second image data; and inputting the second image data into a volumetric fog algorithm model to obtain an image volumetric fog identification result. Optionally, the acquiring the volume information and the pixel information of the original image data includes: differentiating the volume coordinates of the original image data to obtain the volume information; and carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information. Optionally, preprocessing the raw image data according to the range of the volume information to obtain first image data includes: selecting a range of volume information with the occurrence possibility of volume fog according to the original image data; determining a set of image data to be processed in the original image data according to the range of the volume information; performing the preprocessing on the set of image data to be processed to obtain the first image data, wherein the preprocessing comprises: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed. Optionally, the volumetric fog algorithm model includes:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, fig. 3 is a schematic hardware structure of a terminal device according to an embodiment of the present application. As shown in fig. 3, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to enable communication connections between the elements. The memory 33 may comprise a high-speed RAM memory or may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, in which various programs may be stored for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the processor 31 may be implemented as, for example, a central processing unit (Central Processing Unit, abbreviated as CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the processor 31 is coupled to the input device 30 and the output device 32 through wired or wireless connections.
Alternatively, the input device 30 may include a variety of input devices, for example, may include at least one of a user-oriented user interface, a device-oriented device interface, a programmable interface of software, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware insertion interface (such as a USB interface, a serial port, etc.) for data transmission between devices; alternatively, the user-oriented user interface may be, for example, a user-oriented control key, a voice input device for receiving voice input, and a touch-sensitive device (e.g., a touch screen, a touch pad, etc. having touch-sensitive functionality) for receiving user touch input by a user; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, for example, an input pin interface or an input interface of a chip, etc.; optionally, the transceiver may be a radio frequency transceiver chip, a baseband processing chip, a transceiver antenna, etc. with a communication function. An audio input device such as a microphone may receive voice data. The output device 32 may include a display, audio, or the like.
In this embodiment, the processor of the terminal device may include functions for executing each module of the data processing apparatus in each device, and specific functions and technical effects may be referred to the above embodiments and are not described herein again.
Fig. 4 is a schematic hardware structure of a terminal device according to another embodiment of the present application. Fig. 4 is a specific embodiment of the implementation of fig. 3. As shown in fig. 4, the terminal device of the present embodiment includes a processor 41 and a memory 42.
The processor 41 executes the computer program code stored in the memory 42 to implement the methods of the above-described embodiments.
The memory 42 is configured to store various types of data to support operation at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, video, etc. The memory 42 may include a random access memory (random access memory, simply referred to as RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, a processor 41 is provided in the processing assembly 40. The terminal device may further include: a communication component 43, a power supply component 44, a multimedia component 45, an audio component 46, an input/output interface 47 and/or a sensor component 48. The components and the like specifically included in the terminal device are set according to actual requirements, which are not limited in this embodiment.
The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 40 may include one or more modules that facilitate interactions between the processing component 40 and other components. For example, processing component 40 may include a multimedia module to facilitate interaction between multimedia component 45 and processing component 40.
The power supply assembly 44 provides power to the various components of the terminal device. Power supply components 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for terminal devices.
The multimedia component 45 comprises a display screen between the terminal device and the user providing an output interface. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation.
The audio component 46 is configured to output and/or input audio signals. For example, the audio component 46 includes a Microphone (MIC) configured to receive external audio signals when the terminal device is in an operational mode, such as a speech recognition mode. The received audio signals may be further stored in the memory 42 or transmitted via the communication component 43. In some embodiments, audio assembly 46 further includes a speaker for outputting audio signals.
The input/output interface 47 provides an interface between the processing assembly 40 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: volume button, start button and lock button.
The sensor assembly 48 includes one or more sensors for providing status assessment of various aspects for the terminal device. For example, the sensor assembly 48 may detect the open/closed state of the terminal device, the relative positioning of the assembly, the presence or absence of user contact with the terminal device. The sensor assembly 48 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 may also include a camera or the like.
The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi,2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot, where the SIM card slot is used to insert a SIM card, so that the terminal device may log into a GPRS network, and establish communication with a server through the internet.
From the above, it will be appreciated that the communication component 43, the audio component 46, and the input/output interface 47, the sensor component 48 referred to in the embodiment of fig. 4 may be implemented as an input device in the embodiment of fig. 3.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A method for identifying a volume fog of high-precision image data, comprising:
acquiring volume information and pixel information of original image data;
preprocessing the original image data according to the range of the volume information to obtain first image data;
integrating the first image data and the pixel information to obtain second image data;
and inputting the second image data into a volumetric fog algorithm model to obtain an image volumetric fog identification result.
2. The method of claim 1, wherein the acquiring volume information and pixel information of the raw image data comprises:
differentiating the volume coordinates of the original image data to obtain the volume information;
and carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
3. The method of claim 1, wherein preprocessing the raw image data according to the range of the volume information to obtain first image data comprises:
selecting a range of volume information with the occurrence possibility of volume fog according to the original image data;
determining a set of image data to be processed in the original image data according to the range of the volume information;
performing the preprocessing on the set of image data to be processed to obtain the first image data, wherein the preprocessing comprises: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed.
4. The method of claim 1, wherein the volumetric fog algorithm model comprises:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
5. A high precision image data volumetric fog recognition device, comprising:
the acquisition module is used for acquiring volume information and pixel information of the original image data;
the preprocessing module is used for preprocessing the original image data according to the range of the volume information to obtain first image data;
the integration module is used for integrating the first image data and the pixel information to obtain second image data;
and the input module is used for inputting the second image data into the volumetric fog algorithm model to obtain an image volumetric fog identification result.
6. The apparatus of claim 5, wherein the acquisition module comprises:
the differentiating unit is used for differentiating the volume coordinates of the original image data to obtain the volume information;
and the extraction unit is used for carrying out pixel extraction processing on the original image data according to a pixel extraction model to obtain the pixel information.
7. The apparatus of claim 5, wherein the preprocessing module comprises:
a selection unit configured to select a range of volume information having a possibility of occurrence of volume fog from the original image data;
a determining unit for determining a set of image data to be processed in the original image data according to the range of the volume information;
a processing unit, configured to perform the preprocessing on the set of image data to be processed to obtain the first image data, where the preprocessing includes: and performing half-binarization processing, wherein the half-binarization processing is used for carrying out 4 different-precision hierarchical decomposition display on the data of the area to be processed.
8. The apparatus of claim 5, wherein the volumetric fog algorithm model comprises:
wherein [ AR ] represents a volume fog region recognition result set, alpha is a volume fog weight superposition factor, P2 is second image data, and V1-V3 are characteristic parameters of various volume fog possibly occurring.
9. A non-volatile storage medium, characterized in that the non-volatile storage medium comprises a stored program, wherein the program, when run, controls a device in which the non-volatile storage medium is located to perform the method of any one of claims 1 to 4.
10. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for executing the processor, wherein the computer readable instructions when executed perform the method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310317281.2A CN116468883B (en) | 2023-03-27 | 2023-03-27 | High-precision image data volume fog recognition method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310317281.2A CN116468883B (en) | 2023-03-27 | 2023-03-27 | High-precision image data volume fog recognition method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116468883A true CN116468883A (en) | 2023-07-21 |
| CN116468883B CN116468883B (en) | 2023-11-07 |
Family
ID=87181652
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310317281.2A Active CN116468883B (en) | 2023-03-27 | 2023-03-27 | High-precision image data volume fog recognition method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116468883B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6940504B1 (en) * | 2000-11-21 | 2005-09-06 | Microsoft Corporation | Rendering volumetric fog and other gaseous phenomena using an alpha channel |
| CN115293985A (en) * | 2022-08-11 | 2022-11-04 | 北京拙河科技有限公司 | Super-resolution noise reduction method and device for image optimization |
| CN115375582A (en) * | 2022-09-05 | 2022-11-22 | 北京拙河科技有限公司 | Moire digestion method and device based on low-order Taylor decomposition |
-
2023
- 2023-03-27 CN CN202310317281.2A patent/CN116468883B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6940504B1 (en) * | 2000-11-21 | 2005-09-06 | Microsoft Corporation | Rendering volumetric fog and other gaseous phenomena using an alpha channel |
| CN115293985A (en) * | 2022-08-11 | 2022-11-04 | 北京拙河科技有限公司 | Super-resolution noise reduction method and device for image optimization |
| CN115375582A (en) * | 2022-09-05 | 2022-11-22 | 北京拙河科技有限公司 | Moire digestion method and device based on low-order Taylor decomposition |
Non-Patent Citations (1)
| Title |
|---|
| 杨旭;任世卿;苗芳;: "一种改进的基于暗通道先验的图像去雾算法", 沈阳理工大学学报, no. 06 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116468883B (en) | 2023-11-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115623336B (en) | Image tracking method and device for hundred million-level camera equipment | |
| CN115631122A (en) | Image optimization method and device for edge image algorithm | |
| CN116614453B (en) | Image transmission bandwidth selection method and device based on cloud interconnection | |
| CN115409869B (en) | Snow field track analysis method and device based on MAC tracking | |
| CN116468883B (en) | High-precision image data volume fog recognition method and device | |
| CN116228593B (en) | Image perfecting method and device based on hierarchical antialiasing | |
| CN116664413B (en) | Image volume fog eliminating method and device based on Abbe convergence operator | |
| CN116579965B (en) | Multi-image fusion method and device | |
| CN116088580B (en) | Flying object tracking method and device | |
| CN115511735B (en) | Snow field gray scale picture optimization method and device | |
| CN116402935B (en) | Image synthesis method and device based on ray tracing algorithm | |
| CN116389915B (en) | Method and device for reducing flicker of light field camera | |
| CN115546053B (en) | Method and device for eliminating diffuse reflection of graphics on snow in complex terrain | |
| CN116579964B (en) | Dynamic frame gradual-in gradual-out dynamic fusion method and device | |
| CN115187570B (en) | Singular traversal retrieval method and device based on DNN deep neural network | |
| CN115205313B (en) | Picture optimization method and device based on least square algorithm | |
| CN116723419B (en) | Acquisition speed optimization method and device for billion-level high-precision camera | |
| CN116797479B (en) | Image vertical distortion conversion method | |
| CN117871419A (en) | Air quality detection method and device based on optical camera holder control | |
| CN116452481A (en) | Multi-angle combined shooting method and device | |
| CN118096648A (en) | Scene depth measurement method and device | |
| CN116774929A (en) | Data storage method and system based on big data | |
| CN117896625A (en) | Picture imaging method and device based on low-altitude high-resolution analysis | |
| CN116757981A (en) | Multi-terminal image fusion method and device | |
| CN117351341A (en) | Unmanned aerial vehicle fish school identification method and device based on decomposition optimization |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |