A method, device, electronic device and storage medium for optimizing image quality

Abstract

The present application relates to a method, electronic device and storage medium for optimizing image quality, the method comprising: obtaining a visible light image and an infrared image of an image to be optimized; analyzing whether the visible light image and the infrared image need to be optimized; if the analysis result shows that the visible light image and the infrared image need to be optimized, calculating the camera parameter adjustment coefficient, and optimizing the image according to the adjustment coefficient. After obtaining the visible light image and the infrared image of the image to be optimized, the present application performs contrast analysis, fuzzy analysis, and histogram statistics on the visible light image and the infrared image to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results to determine whether the visible light image and the infrared image need to be optimized. If optimization is required, the corresponding camera parameter adjustment coefficient is calculated, and the image is optimized according to the adjustment coefficient. Compared with the prior art, the present application proposes a dynamic imaging parameter adjustment strategy based on real-time feedback of image quality that is independent of hardware resources.

Description

Technical Field

[0001] The present application relates to the field of image quality technology, and in particular to a method, device, electronic device and storage medium for optimizing image quality. Background Art

[0002] Binocular face devices include visible light cameras and infrared cameras. The performance of face detection and recognition systems depends on the image quality of the imaging device. Existing devices rely on the information of the device's hardware sensors to optimize image quality, such as wide dynamic imaging devices, to improve the image quality under different external environments (lighting). Relying on hardware sensor information places high demands on the device and cannot use the device's binocular imaging characteristics to comprehensively improve the image quality. Summary of the invention

[0003] Based on the above problems, the present application provides a method, electronic device and storage medium for optimizing image quality.

[0004] In a first aspect, an embodiment of the present application provides a method for optimizing image quality, including:

[0005] Acquire a visible light image and an infrared image of the image to be optimized;

[0006] Analyze whether visible light images and infrared images need to be optimized;

[0007] If the analysis result shows that the visible light image and the infrared image need to be optimized, the camera parameter adjustment coefficients are calculated and the images are optimized according to the adjustment coefficients.

[0008] Furthermore, the above method for optimizing image quality further includes:

[0009] If the analysis results show that visible light images and infrared images need to be optimized;

[0010] Compensation analysis is performed on visible light images and infrared images to obtain optimized images.

[0011] Furthermore, in the above method for optimizing image quality, analyzing whether the visible light image and the infrared image need to be optimized includes:

[0012] Detecting the face area in the image to be optimized;

[0013] Determine the location of the face area in the image to be optimized;

[0014] Contrast analysis, fuzzy analysis and histogram statistics are performed on the face area to obtain contrast analysis results, fuzzy analysis results and histogram statistics results.

[0015] Determine whether the visible light image and the infrared image need to be optimized according to the contrast analysis results, the fuzzy analysis results and the histogram statistics results as well as the preset contrast threshold, the preset fuzzy analysis threshold and the preset histogram statistics results.

[0016] Furthermore, in the above-mentioned method for optimizing image quality, determining whether the visible light image and the infrared image need to be optimized according to the contrast analysis result, the blur analysis result and the histogram statistical result and the preset contrast threshold, the preset blur analysis threshold and the preset histogram statistical threshold includes:

[0017] Whether the visible light image and the infrared image need to be optimized is determined according to the size of the contrast analysis result and the contrast threshold, the size of the fuzzy analysis result and the preset fuzzy analysis threshold, and the size of the histogram statistics result and the preset histogram statistics threshold.

[0018] Furthermore, in the above-mentioned method for optimizing image quality, calculating the camera parameter adjustment coefficient includes:

[0019] The camera parameter adjustment coefficient is calculated based on the analysis results and the imaging adjustment parameter table of the camera corresponding device calibration.

[0020] Furthermore, in the above method for optimizing image quality, performing compensation analysis on the visible light image and the infrared image to obtain an optimized image includes:

[0021] Correcting the face region images in the visible light image and the infrared image to obtain a corrected face region image;

[0022] Input the corrected face area image into the multi-task model to output the enhanced face area image and fusion coefficient;

[0023] An optimized image is obtained according to the face region images in the visible light image and the infrared image, the enhanced face region and the fusion coefficient.

[0024] Furthermore, in the above method for optimizing image quality, obtaining an optimized image according to the face region images in the visible light image and the infrared image, the enhanced face region, and the fusion coefficient includes:

[0025] The face region images in the visible light image and the infrared image as well as the enhanced face region are fused through the fusion coefficient to obtain an optimized image.

[0026] In a second aspect, an embodiment of the present application further provides a device for optimizing image quality, including:

[0027] Acquisition module: used to acquire visible light image and infrared image of the image to be optimized;

[0028] Analysis module: used to analyze whether visible light images and infrared images need to be optimized;

[0029] Calculation module and optimization module: used to calculate the camera parameter adjustment coefficients and optimize the images according to the adjustment coefficients if the analysis results show that the visible light image and the infrared image need to be optimized.

[0030] In a third aspect, an embodiment of the present application further provides an electronic device, characterized in that it includes: a processor and a memory;

[0031] The processor is used to execute the above-mentioned method for optimizing image quality by calling the program or instruction stored in the memory.

[0032] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a program or instruction, and the program or instruction enables the computer to perform the above-mentioned method of optimizing image quality.

[0033] The advantages of the embodiments of the present application are: the present application relates to a method, electronic device and storage medium for optimizing image quality, the method comprising: obtaining a visible light image and an infrared image of the image to be optimized; analyzing whether the visible light image and the infrared image need to be optimized; if the analysis result is that the visible light image and the infrared image need to be optimized, calculating the camera parameter adjustment coefficient, and optimizing the image according to the adjustment coefficient. After obtaining the visible light image and the infrared image of the image to be optimized, the present application performs contrast analysis, fuzzy analysis, and histogram statistics on the visible light image and the infrared image to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results to determine whether the visible light image and the infrared image need to be optimized. If optimization is required, the corresponding camera parameter adjustment coefficient is calculated, and the image is optimized according to the adjustment coefficient. A dynamic imaging parameter adjustment strategy based on real-time feedback of image quality that is independent of hardware resources is proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 A method for optimizing image quality provided in an embodiment of the present application is shown in FIG. Figure 1 ;

[0036] Figure 2 A method for optimizing image quality provided in an embodiment of the present application is shown in FIG. Figure 2 ;

[0037] Figure 3 A method for optimizing image quality provided in an embodiment of the present application is shown in FIG. Figure 3 ;

[0038] Figure 4 A schematic diagram of a device for optimizing image quality provided in an embodiment of the present application;

[0039] Figure 5 It is a schematic block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific implementation disclosed below.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0042] Figure 1 A method for optimizing image quality provided in an embodiment of the present application is shown in FIG. Figure 1 .

[0043] In a first aspect, the present application provides a method for optimizing image quality, combining Figure 1 , including three steps from S101 to S103:

[0044] S101: Acquire a visible light image and an infrared image of an image to be optimized.

[0045] Specifically, in the embodiment of the present application, the visible light image and the infrared image of the image to be optimized are obtained by obtaining frame-synchronized binocular image frames through an imaging device module.

[0046] S102: Analyze whether the visible light image and the infrared image need to be optimized.

[0047] Specifically, in the embodiment of the present application, by performing contrast analysis, fuzzy analysis, and histogram statistics on the visible light image and the infrared image, it is determined whether the visible light image and the infrared image need to be optimized to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results.

[0048] S103: If the analysis result shows that the visible light image and the infrared image need to be optimized, the camera parameter adjustment coefficients are calculated, and the images are optimized according to the adjustment coefficients.

[0049] Specifically, in the embodiment of the present application, if the analysis result shows that the visible light image and the infrared image need to be optimized, the corresponding camera parameter adjustment coefficient is calculated based on the analysis result of the quality of the visible light image and the infrared image, and the image is optimized according to the adjustment coefficient. A dynamic imaging parameter adjustment strategy based on real-time feedback of image quality that does not rely on hardware resources is proposed to solve the imaging quality problem of conventional equipment under complex external environmental conditions.

[0050] Furthermore, the above method for optimizing image quality further includes:

[0051] If the analysis result shows that the visible light image and the infrared image need to be optimized, a compensation analysis is performed on the visible light image and the infrared image to obtain an optimized image.

[0052] Specifically, in the embodiment of the present application, if the analysis result shows that the visible light image and the infrared image need to be optimized, the optimized image can also be obtained by a compensation analysis method, which is described in detail below in conjunction with the specific steps.

[0053] Figure 2 A method for optimizing image quality provided in an embodiment of the present application is shown in FIG. Figure 2 .

[0054] Furthermore, in the above method for optimizing image quality, it is analyzed whether the visible light image and the infrared image need to be optimized, and combined with Figure 2 , including four steps from S201 to S204:

[0055] S201: Detecting a face region in an image to be optimized.

[0056] Specifically, in the embodiment of the present application, if the analysis result is that the visible light image and the infrared image need to be optimized, the face areas b and c are detected respectively on the synchronized binocular image frames.

[0057] S202: Determine the position of the face region in the image to be optimized.

[0058] Specifically, in the embodiment of the present application, after respectively detecting the face regions b and c in the synchronized binocular image frames, the positions of the face regions are further determined.

[0059] S203: performing contrast analysis, fuzzy analysis, and histogram statistics on the face region to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results.

[0060] Specifically, in the embodiment of the present application, after the position of the face area is determined, contrast analysis, fuzzy analysis, and histogram statistics are performed on the face area to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results.

[0061] S204: Determine whether the visible light image and the infrared image need to be optimized according to the contrast analysis result, the blur analysis result, the histogram statistical result, and the preset contrast threshold, the preset blur analysis threshold, and the preset histogram statistical result.

[0062] Specifically, in an embodiment of the present application, contrast analysis, fuzzy analysis, and histogram statistics are performed on the face area to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results, and then compared with a preset contrast threshold, a preset fuzzy analysis threshold, and a preset histogram statistics result to determine whether the visible light image and the infrared image need to be optimized.

[0063] Furthermore, in the above-mentioned method for optimizing image quality, determining whether the visible light image and the infrared image need to be optimized according to the contrast analysis result, the blur analysis result and the histogram statistical result and the preset contrast threshold, the preset blur analysis threshold and the preset histogram statistical threshold includes:

[0064] Whether the visible light image and the infrared image need to be optimized is determined according to the size of the contrast analysis result and the contrast threshold, the size of the fuzzy analysis result and the preset fuzzy analysis threshold, and the size of the histogram statistics result and the preset histogram statistics threshold.

[0065] Specifically, in the embodiment of the present application, for example, if the contrast analysis result is greater than the contrast threshold, the blur analysis result is greater than the preset blur analysis threshold, and the histogram statistical result is greater than the preset histogram statistical threshold, then the visible light image and the infrared image do not need to be optimized; if the contrast analysis result is less than the contrast threshold, the blur analysis result is less than the preset blur analysis threshold, and the histogram statistical result is less than the preset histogram statistical threshold, then the visible light image and the infrared image do not need to be optimized. It should be understood that the above situations are just examples, and there may be other situations, which are not listed here one by one.

[0066] Furthermore, in the above-mentioned method for optimizing image quality, calculating the camera parameter adjustment coefficient includes:

[0067] The camera parameter adjustment coefficient is calculated based on the analysis results and the imaging adjustment parameter table of the camera corresponding device calibration.

[0068] Specifically, in the embodiment of the present application, based on the quality analysis result of the current image frame and according to the imaging adjustment parameter table calibrated by the device, the corresponding camera parameter adjustment coefficient is calculated, and the image is optimized according to the adjustment coefficient.

[0069] Figure 3 A method for optimizing image quality provided in an embodiment of the present application is shown in FIG. Figure 3 .

[0070] Furthermore, in the above method for optimizing image quality, the visible light image and the infrared image are compensated and analyzed to obtain an optimized image, and the Figure 3 , including three steps from S301 to S303:

[0071] S301: Correcting the face region images in the visible light image and the infrared image to obtain a corrected face region image;

[0072] S302: Input the corrected face region image into the multi-task model to output the enhanced face region image and fusion coefficient;

[0073] S303: Obtain an optimized image according to the face region images in the visible light image and the infrared image, the enhanced face region, and the fusion coefficient.

[0074] Specifically, in an embodiment of the present application, the face areas are detected for the synchronized binocular image frames, and the face areas b and c and the corresponding correction key points P_b and P_c are obtained respectively. The face areas are corrected respectively to obtain the corrected face image areas P_B and P_C. A multi-task model is trained, and the input is P_B and P_C. The output is the enhanced face areas E_P_B and E_P_C, and the fusion coefficient alpha. The optimized image is obtained according to the correction key points P_b and P_c of the face area images in the visible light image and the infrared image, the enhanced face areas E_P_B and E_P_C, and the fusion coefficient alpha.

[0075] Furthermore, in the above method for optimizing image quality, obtaining an optimized image according to the face region images in the visible light image and the infrared image, the enhanced face region, and the fusion coefficient includes:

[0076] The face region images in the visible light image and the infrared image as well as the enhanced face region are fused through the fusion coefficient to obtain an optimized image.

[0077] Specifically, in the embodiment of the present application, the enhanced face areas E_P_B and E_P_C generated by the multi-task model are fused with the corrected key points P_b and P_c of the original face areas through the fusion coefficient alpha fusion to obtain the final optimized image.

[0078] F_E_P_B=alpha*E_P_B+(1-alpha)*P_b

[0079] F_E_P_C=alpha*E_P_C+(1-alpha)*P_c

[0080] It should be understood that the present application also proposes a compensatory image quality improvement solution based on the characteristics of the binocular camera, which makes full use of the imaging differences of the RGB camera and the IR camera in different environments, and uses a multi-task modeling method to obtain optimized picture frames.

[0081] Figure 4 A schematic diagram of a device for optimizing image quality provided in an embodiment of the present application.

[0082] In a second aspect, an embodiment of the present application further provides a device for optimizing image quality, including:

[0083] Acquisition module 401: used to acquire the visible light image and infrared image of the image to be optimized.

[0084] Specifically, in the embodiment of the present application, the acquisition module 401 acquires the visible light image and the infrared image of the image to be optimized by acquiring frame-synchronized binocular image frames through the imaging device module.

[0085] Analysis module 402: used to analyze whether the visible light image and the infrared image need to be optimized.

[0086] Specifically, in the embodiment of the present application, the analysis module 402 determines whether the visible light image and the infrared image need to be optimized by performing contrast analysis, fuzzy analysis, and histogram statistics on the visible light image and the infrared image to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results.

[0087] The calculation module 403 and the optimization module 404 are used to calculate the camera parameter adjustment coefficients and optimize the images according to the adjustment coefficients if the analysis result shows that the visible light image and the infrared image need to be optimized.

[0088] Specifically, in the embodiment of the present application, the calculation module 403 calculates the corresponding camera parameter adjustment coefficient based on the analysis results of the quality of the visible light image and the infrared image, and the optimization module 404 optimizes the image according to the adjustment coefficient. The present application proposes a dynamic imaging parameter adjustment strategy based on real-time feedback of image quality that does not rely on hardware resources, and solves the imaging quality problem of conventional equipment under complex external environmental conditions.

[0089] In a third aspect, an embodiment of the present application further provides an electronic device, characterized in that it includes: a processor and a memory;

[0090] The processor is used to execute the above-mentioned method for optimizing image quality by calling the program or instruction stored in the memory.

[0091] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a program or instruction, and the program or instruction enables the computer to perform the above-mentioned method of optimizing image quality.

[0092] Figure 5 It is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.

[0093] like Figure 5 As shown, the electronic device includes: at least one processor 501, at least one memory 502 and at least one communication interface 503. The various components in the electronic device are coupled together through a bus system 504. The communication interface 503 is used to transmit information between external devices. It can be understood that the bus system 504 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 504 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 5 Various buses are labeled as bus system 504 .

[0094] It can be understood that the memory 502 in this embodiment can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0095] In some implementations, the memory 502 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.

[0096] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. A program that implements any of the methods for optimizing image quality provided in an embodiment of the present application can be included in the application.

[0097] In an embodiment of the present application, the processor 501 calls a program or instruction stored in the memory 502, specifically, a program or instruction stored in an application, and the processor 501 is used to execute the steps of each embodiment of a method for optimizing image quality provided in an embodiment of the present application.

[0098] Acquire a visible light image and an infrared image of the image to be optimized;

[0099] Analyze whether visible light images and infrared images need to be optimized;

[0100] If the analysis result shows that the visible light image and the infrared image need to be optimized, the camera parameter adjustment coefficients are calculated and the images are optimized according to the adjustment coefficients.

[0101] Any of the methods for optimizing image quality provided in the embodiments of the present application can be applied to the processor 501, or implemented by the processor 501. The processor 501 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 501 or instructions in the form of software. The above processor 501 can be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0102] The steps of any method in a method for optimizing image quality provided in an embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software units in a decoding processor. The software unit can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 502, and the processor 501 reads the information in the memory 502, and completes the steps of a method for optimizing image quality in combination with its hardware.

[0103] Those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present application and to form different embodiments.

[0104] Those skilled in the art will appreciate that the description of each embodiment has its own emphasis, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0105] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for optimizing image quality, characterized in that: include: Acquire a visible light image and an infrared image of the image to be optimized; Analyzing whether the visible light image and the infrared image need to be optimized, comprising: detecting a face area in the image to be optimized, determining the face area in the image to be optimized, performing contrast analysis, fuzzy analysis, and histogram statistics on the face area to obtain a contrast analysis result, a fuzzy analysis result, and a histogram statistics result, and determining whether the visible light image and the infrared image need to be optimized according to the contrast analysis result, the fuzzy analysis result, the histogram statistics result, and a preset contrast threshold, a preset fuzzy analysis threshold, and a preset histogram statistics threshold; If the analysis result shows that the visible light image and the infrared image need to be optimized, calculating the camera parameter adjustment coefficient, including: calculating the camera parameter adjustment coefficient according to the analysis result and the imaging adjustment parameter table calibrated by the camera corresponding device, and optimizing the image according to the adjustment coefficient; If the analysis result shows that the visible light image and the infrared image need to be optimized, a compensation analysis is performed on the visible light image and the infrared image to obtain an optimized image, including: correcting the face area images in the visible light image and the infrared image to obtain a corrected face area image, inputting the corrected face area image into a multi-task model to output an enhanced face area image and a fusion coefficient, and obtaining an optimized image based on the face area images in the visible light image and the infrared image, the enhanced face area, and the fusion coefficient.

2. The method for optimizing image quality according to claim 1, characterized in that: The determining whether the visible light image and the infrared image need to be optimized according to the contrast analysis result, the blur analysis result, the histogram statistical result, the preset contrast threshold, the preset blur analysis threshold, and the preset histogram statistical threshold includes: Whether the visible light image and the infrared image need to be optimized is determined according to the difference between the contrast analysis result and the contrast threshold, the difference between the fuzzy analysis result and the preset fuzzy analysis threshold, and the difference between the histogram statistics result and the preset histogram statistics threshold.

3. The method for optimizing image quality according to claim 1, characterized in that: The optimized image is obtained according to the face region image in the visible light image and the infrared image, the enhanced face region, and the fusion coefficient, including: The visible light image, the face region image in the infrared image, and the enhanced face region are fused through a fusion coefficient to obtain an optimized image.

4. A device for optimizing image quality, characterized in that: include: Acquisition module: used to acquire visible light image and infrared image of the image to be optimized; Analysis module: used for analyzing whether the visible light image and the infrared image need to be optimized, including: detecting the face area in the image to be optimized, determining the face area in the image to be optimized, performing contrast analysis, fuzzy analysis, and histogram statistics on the face area to obtain contrast analysis results, fuzzy analysis results, and histogram statistics results, and determining whether the visible light image and the infrared image need to be optimized according to the contrast analysis results, the fuzzy analysis results, the histogram statistics results, and a preset contrast threshold, a preset fuzzy analysis threshold, and a preset histogram statistics threshold; The calculation module and the optimization module are used to calculate the camera parameter adjustment coefficient if the analysis result shows that the visible light image and the infrared image need to be optimized, including: calculating the camera parameter adjustment coefficient according to the analysis result and the imaging adjustment parameter table calibrated by the camera corresponding device, and optimizing the image according to the adjustment coefficient; If the analysis result shows that the visible light image and the infrared image need to be optimized, compensation analysis is performed on the visible light image and the infrared image to obtain an optimized image, including: correcting the face area images in the visible light image and the infrared image to obtain a corrected face area image, inputting the corrected face area image into a multi-task model to output an enhanced face area image and a fusion coefficient, and obtaining an optimized image based on the face area images in the visible light image and the infrared image, the enhanced face area, and the fusion coefficient.

5. An electronic device, characterized in that: include: Processor and memory; The processor is used to execute the method for optimizing image quality as claimed in any one of claims 1 to 3 by calling the program or instruction stored in the memory.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, and the program or the instruction enables a computer to execute the method for optimizing image quality as claimed in any one of claims 1 to 3.

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