Infrared image detail enhancement method, electronic equipment and medium

Abstract

The invention belongs to the technical field of image processing, and particularly relates to an infrared image detail enhancement method, electronic equipment and a medium, and the method comprises the steps: obtaining a to-be-enhanced infrared image, and carrying out the filtering processing of the infrared image, and generating a filtered image; filtering the filtered image to generate a basic image; performing contrast enhancement on the basic image to generate a contrast enhanced image; generating a noise image according to the infrared image and the filtered image; generating an edge image according to the filtered image and the basic image; performing compression processing on the contrast enhanced image to generate an 8-bit basic image; and performing weighted fusion on the 8-bit basic image, the edge image and the noise image to generate an output image. According to the scheme, real edge details of the infrared image can be fully reserved and enhanced, noise interference is effectively restrained, and the generated output image has clearer text detail display, smoother background transition and a better visual effect.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and particularly relates to an infrared image detail enhancement method, an electronic device, and a medium. Background Art

[0002] Infrared images often lack details and are blurred. Detail enhancement algorithms can effectively enhance the details of infrared images, making the infrared images clearer and more delicate, and having a wide range of application prospects. In related technologies, infrared image detail enhancement methods generally rely on image layering technology. That is to say, an infrared image is divided into a base layer and a detail layer, and by magnifying the detail layer, the high-frequency detail information of the infrared image is enhanced. However, since there is also noise information in the detail layer in addition to edge information, this method will also amplify the noise while enhancing the details.

[0003] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of the present disclosure, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art.

[0004] Application Content

[0005] In view of at least one of the above technical problems, this application provides an infrared image detail enhancement method, an electronic device, and a medium, which solve the problem in related technologies that an infrared image is divided into a base layer and a detail layer, and by magnifying the detail layer, the high-frequency detail information of the infrared image is enhanced. However, since there is also noise information in the detail layer in addition to edge information, this method will also amplify the noise while enhancing the details.

[0006] In a first aspect, this application provides an infrared image detail enhancement method, and the method includes:

[0007] Obtain an infrared image to be enhanced, and perform filtering processing on the infrared image to generate a filtered image;

[0008] Perform filtering processing on the filtered image to generate a base image;

[0009] Perform contrast enhancement on the base image to generate a contrast-enhanced image;

[0010] Generate a noise image according to the infrared image and the filtered image;

[0011] Generate an edge image according to the filtered image and the base image;

[0012] Perform compression processing on the contrast-enhanced image to generate an 8-bit base image;

[0013] Perform weighted fusion on the 8-bit base image, the edge image, and the noise image to generate an output image.

[0014] According to the infrared image detail enhancement method of the present application, through multi-level filtering, noise feature separation, and dynamic weighted fusion, etc., first, two filtering processes are adopted to generate a filtered image and a basic image in sequence, forming a more accurate image hierarchical structure, effectively distinguishing feature information at different levels; by enhancing the contrast and compressing the dynamic range of the basic layer, while improving the overall image contrast, the compatibility with standard display devices is ensured; further, by differentially extracting the noise image and the edge image, the efficient separation of noise features and edge details is achieved, enabling the individual adjustment of the enhancement intensity for different image components during the weighted fusion stage, and finally solving the contradiction problem of noise amplification and detail enhancement in the traditional method in terms of output quality. Compared with the traditional hierarchical enhancement technology, this solution can not only fully retain and enhance the real edge details of the infrared image, but also effectively suppress noise interference. The generated output image has clearer text details display, smoother background transition, and better visual effects, significantly improving the image usability and application value of the infrared imaging system in complex scenarios such as low illuminance and high noise.

[0015] In some possible implementation manners, generating the filtered image includes:

[0016] Performing edge-preserving filtering on the infrared image to generate the filtered image.

[0017] In some possible implementation manners, the edge-preserving filtering is one of guided filtering, bilateral filtering, or non-local means filtering.

[0018] In some possible implementation manners, generating the basic image includes:

[0019] Performing smoothing filtering on the filtered image to generate the basic image.

[0020] In some possible implementation manners, the smoothing filtering is mean filtering or Gaussian filtering.

[0021] In some possible implementation manners, generating the noise image includes:

[0022] Subtracting the filtered image from the infrared image to generate the noise image.

[0023] In some possible implementation manners, generating the edge image includes:

[0024] Subtracting the basic image from the filtered image to generate the edge image.

[0025] In some possible implementation manners, generating the 8-bit basic image includes:

[0026] Calculating the minimum average value and the maximum average value of the pixels of the infrared image;

[0027] Generate a compression range based on the minimum average value and the maximum average value of pixels.

[0028] Calculate the minimum pixel value and the maximum pixel value of the first filtered image.

[0029] Compress the contrast-enhanced image into an 8-bit base image according to the minimum pixel value, the maximum pixel value, and the compression range.

[0030] In a second aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, an infrared image detail enhancement method is implemented.

[0031] In a third aspect, the present application provides a computer-readable storage medium for storing a computer program, characterized in that when the computer program is executed by a processor, an infrared image detail enhancement method is implemented.

[0032] The following further describes the present application in conjunction with the drawings and embodiments. Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a schematic flowchart of the infrared image detail enhancement method according to an embodiment of the present application;

[0035] Figure 2 It is a schematic structural diagram of an electronic device for implementing the infrared image detail enhancement method according to an embodiment of the present application; Detailed Embodiments

[0036] To make the above objects, features, and advantages of the present application more obvious and understandable, the following will give a detailed description of the specific embodiments of the present application in conjunction with the drawings. Many specific details are set forth in the following description in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein. Those skilled in the art can make similar improvements without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0037] As Figure 1 shown, this embodiment provides an infrared image detail enhancement method, and the method includes: step S100 to step S700.

[0038] Step S100: Obtain the infrared image to be enhanced, and perform filtering on the infrared image to generate a filtered image.

[0039] It can be understood that the filtering of the infrared image is mainly edge-preserving filtering. Among them, the edge-preserving filtering is one of guided filtering, bilateral filtering or non-local means filtering. Edge-preserving filtering is through the adaptive weighting of the local neighborhood (such as bilateral filtering) or gradient-based diffusion (such as anisotropic filtering), without over-blurring the edge area when smoothing the uniform area, so as to maintain the integrity of key information such as the target contour and temperature boundary.

[0040] Step S200: Perform filtering on the filtered image to generate a base image.

[0041] It can be understood that the filtering of the filtered image is mainly smoothing filtering. The smoothing filtering is mean filtering or Gaussian filtering.

[0042] It should be noted that the key information of the infrared image (such as the target contour, temperature boundary, etc.) is concentrated in the edge area, while the uniform area is vulnerable to noise interference. Edge-preserving filtering (such as guided filtering, bilateral filtering) can avoid over-blurring the edge area while smoothing the noise in the uniform area through the adaptive weighting or gradient diffusion mechanism, ensuring the integrity of the edge information (such as the temperature mutation boundary), and retaining the key structural features for subsequent processing. If direct smoothing filtering (such as mean, Gaussian filtering) is performed, the edge will be blurred due to the lack of an edge protection mechanism, destroying the basic structure of the image.

[0043] In this embodiment, the order of the two filterings forms a progressive processing logic of "first edge-preserving separation and then smoothing layering". The filtered image generated by the edge-preserving filtering has achieved the preliminary separation of noise and edges (retaining edges and suppressing noise in the uniform area); on this basis, smoothing filtering is performed to further deeply smooth the low-frequency uniform area in the filtered image, generating a base image that only contains basic low-frequency information (such as background gray level, gentle gray level change), while the edge information is effectively retained in the subsequent processing steps (such as generating an edge image in step S500) through the difference between the two filterings. This layering strategy avoids the contradiction of "insufficient edge protection" or "incomplete noise suppression" in traditional single-layer filtering, providing a clear layering basis for subsequent contrast enhancement (for the base image), noise separation (based on the difference between the infrared image and the filtered image), and edge enhancement (based on the difference between the filtered image and the base image).

[0044] Step S300: Perform contrast enhancement on the base image to generate a contrast-enhanced image.

[0045] Specifically, histogram equalization is used to perform contrast enhancement on the base image.

[0046] It is understandable that the base image is the low-frequency component obtained after edge-preserving filtering and smoothing filtering, mainly containing basic information such as the background gray level of the image and the gray level changes in flat areas. Due to the effect of smoothing filtering, the base image may have the problem of insufficient contrast - the gray level differences in uniform or gradually changing areas are further weakened, resulting in a blurred overall visual effect (such as reduced distinguishability of background areas with different temperatures). Contrast enhancement can effectively increase the gray level dynamic range in the low-frequency region of the base image, making the levels in the background (such as areas with different temperature gradients) more distinct.

[0047] Moreover, in the subsequent steps, the base image (compressed to 8 bits) after contrast enhancement, the edge image, and the noise image need to be weighted and fused. The base image after contrast enhancement can use a more distinct low-frequency background as the fusion carrier, ensuring that the high-frequency details of the edge image (such as target contours, temperature boundaries) have a clearer reference benchmark when superimposed, avoiding visual conflicts between edge enhancement and the blurred background. At the same time, the enhanced contrast can reduce the loss of basic information during the compression process (step S600), ensuring that the 8-bit base image can be naturally fused with the detail enhancement effect of the edge image while retaining sufficient gray levels, ultimately generating a high-quality infrared image with rich levels and prominent details.

[0048] Step S400: Generate a noise image based on the infrared image and the filtered image;

[0049] Specifically, subtract the filtered image from the infrared image to generate a noise image. It is understandable that the infrared image is extracted to generate a noise image.

[0050] Step S500: Generate an edge image based on the filtered image and the base image;

[0051] Specifically, subtract the base image from the filtered image to generate an edge image. It is understandable that the filtered image is extracted to generate an edge image.

[0052] Step S600: Perform compression processing on the contrast-enhanced image to generate an 8-bit base image.

[0053] It is understandable that the 8-bit base image can form an implicit constraint on the weighting coefficient of the noise image by limiting the gray level range, that is, only allowing noise amplitudes that match the 8-bit dynamic range to participate in the fusion. Thus, without adding an additional noise suppression algorithm, using the characteristics of bit-depth compression, it can naturally suppress the excessive enhancement of low-amplitude noise, and cooperate with the targeted enhancement of the edge image to achieve the dual effects of edge preservation and noise suppression.

[0054] Step S700: Perform weighted fusion on the 8-bit base image, the edge image, and the noise image to generate an output image.

[0055] It can be understood that the clear background hierarchy of the 8-bit base image provides a highly recognizable display substrate for edge details, avoiding the visual defect of blurred background while enhancing details in traditional methods. The independently extracted edge image only contains effective high-frequency signals, and its enhancement process is not interfered by noise. Compared with the enhancement mode of "detail layer aliasing noise" in traditional methods, the edge sharpness is increased by more than 30%. The independently extracted noise image can be specifically weakened, and the standard deviation of the noise in the fused output image is reduced by 40% compared with traditional methods, significantly improving the signal-to-noise ratio of the image. In this way, through weighted fusion, the tripartite cooperation of low-frequency background optimization, high-frequency edge enhancement, and noise component suppression is achieved, solving the problem of synchronous amplification of details and noise in the prior art.

[0056] The infrared image detail enhancement method of this embodiment, through multi-level filtering, noise feature separation, and dynamic weighted fusion, etc., first uses two filtering processes to sequentially generate a filtered image and a base image, forming a more accurate image layering structure, effectively distinguishing feature information at different levels; by enhancing the contrast and compressing the dynamic range of the base layer, while improving the overall image contrast, the compatibility with standard display devices is ensured; further, by differentially extracting the noise image and the edge image, the efficient separation of noise features and edge details is realized, enabling the enhancement intensity to be individually regulated for different image components during the weighted fusion stage, and finally solving the contradiction problem of noise amplification and detail enhancement in traditional methods in terms of output quality. Compared with traditional layering enhancement techniques, this solution can not only fully retain and enhance the real edge details of infrared images, but also effectively suppress noise interference. The generated output image has clearer text details display, smoother background transition, and better visual effects, significantly improving the image usability and application value of infrared imaging systems in complex scenarios such as low illumination and high noise.

[0057] As Figure 1 shown, in some embodiments, generating an 8-bit base image includes: step S610 to step S640.

[0058] Step 610, calculate the pixel minimum average value and the pixel maximum average value of the infrared image.

[0059] Among them, the pixel minimum average value is the average value of the 100 smallest pixel values in the infrared image. The pixel maximum average value is the average value of the 100 largest pixel values in the infrared image.

[0060] Step 620, generate a compression range according to the pixel minimum average value and the pixel maximum average value.

[0061] Use the following formula to calculate the compression range:

[0062]

[0063] Wherein, range is the compression range, maxValue is the maximum average value of pixels, minValue is the minimum average value of pixels, and s is an adjustable compression coefficient.

[0064] Step 630: Calculate the minimum and maximum pixel values of the first filtered image.

[0065] Step 640: Compress the contrast-enhanced image into an 8-bit base image according to the minimum pixel value, the maximum pixel value, and the compression range.

[0066] Generate the 8-bit base image using the following formula:

[0067]

[0068] Wherein, x is the pixel value of any point in the contrast-enhanced image, x' represents the pixel value of the corresponding point in the 8-bit base image, maxValue' is the maximum pixel value, minValue' is the minimum pixel value, and range is the compression range.

[0069] That is to say, by performing compression calculations on each point of the contrast-enhanced image, an 8-bit base image is finally generated.

[0070] To implement the above embodiments, the present application also provides an electronic device and a computer-readable storage medium.

[0071] In a second aspect, the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, an infrared image detail enhancement method is implemented.

[0072] In a third aspect, the present application provides a computer-readable storage medium for storing a computer program, characterized in that when the computer program is executed by a processor, an infrared image detail enhancement method is implemented.

[0073] Figure 2 It is a schematic structural diagram of an electronic device for implementing the infrared image detail enhancement method in the embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described herein and / or claimed.

[0074] As Figure 2As shown, the electronic device includes: a memory 610, a processor 620, and a computer program 630 stored on the memory and executable on the processor. Each component is interconnected using different buses and can be mounted on a common motherboard or otherwise installed as required. The processor can process instructions executed within the electronic device, including instructions for storing graphical information in the memory or on the memory for displaying a GUI on an external input / output device (such as a display device coupled to an interface). In other embodiments, multiple processors and / or multiple buses can be used in conjunction with multiple memories and multiple memories if needed. Similarly, multiple electronic devices can be connected, with each device providing part of the necessary operations (such as an array of servers, a set of blade servers, or a multiprocessor system).

[0075] The memory 610 is the non-transitory computer-readable storage medium provided by the present application. Among them, the memory stores instructions executable by at least one processor, enabling at least one processor to execute the method of the above embodiments. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to execute the method of the above embodiments.

[0076] The memory 610, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the method in the above embodiments. The processor 620 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 610, thereby implementing the method in the above embodiments.

[0077] The memory 610 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the electronic device for implementing the method in the above embodiments, etc. In addition, the memory 610 may include high-speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory 610 may optionally include a memory remotely located relative to the processor 620, and these remote memories can be connected to the electronic device for implementing the method in the above embodiments through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0078] The electronic device according to the method in the above embodiments may further include: an input device 640 and an output device 650. The processor 620, the memory 610, the input device 640, and the output device 650 can be connected through a bus or other means. Figure 2Take the bus connection as an example.

[0079] The input device 640 can receive input digital or character information and generate key signal inputs related to the user settings and function controls of the electronic device, such as input devices like touchscreens, keypads, mice, trackpads, touchpads, pointing sticks, one or more mouse buttons, trackballs, joysticks, etc. The output device 650 can include display devices, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors), etc. The display device can include, but is not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device can be a touchscreen.

[0080] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0081] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0082] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions can be executed in a manner that is not shown or discussed in sequence, including in a substantially simultaneous manner according to the functions involved or in the reverse order, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0083] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing when necessary, and then stored in a computer memory.

[0084] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0085] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments. The above-mentioned storage medium can be a read-only memory, a disk, or an optical disc, etc.

[0086] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0087] The above are only the preferred embodiments of the present application and do not impose any form of limitation on the present application. Any person skilled in the art can make many possible changes and modifications to the technical solution of the present application, or modify it into an equivalent embodiment with equivalent changes, without departing from the scope of the technical solution of the present application. Therefore, all equivalent changes made according to the shape, structure and principle of the present application without departing from the content of the technical solution of the present application shall be covered by the protection scope of the present application.

Claims

1. An infrared image detail enhancement method, characterized in that The method includes: Obtain an infrared image to be enhanced, and perform filtering processing on the infrared image to generate a filtered image; Perform filtering processing on the filtered image to generate a base image; Perform contrast enhancement on the base image to generate a contrast-enhanced image; Generate a noise image according to the infrared image and the filtered image; Generate an edge image according to the filtered image and the base image; Perform compression processing on the contrast-enhanced image to generate an 8-bit base image; Perform weighted fusion on the 8-bit base image, the edge image, and the noise image to generate an output image.

2. The infrared image detail enhancement method according to claim 1, characterized in that The generating of the filtered image includes: Perform edge-preserving filtering on the infrared image to generate the filtered image.

3. The infrared image detail enhancement method according to claim 2, wherein The edge-preserving filtering is one of guided filtering, bilateral filtering, or non-local means filtering.

4. The infrared image detail enhancement method according to claim 1, characterized in that The generating of the base image includes: Perform smoothing filtering on the filtered image to generate the base image.

5. The infrared image detail enhancement method according to claim 4, wherein The smoothing filtering is mean filtering or Gaussian filtering.

6. The infrared image detail enhancement method according to claim 1, characterized in that The generating of the noise image includes: Subtract the filtered image from the infrared image to generate the noise image.

7. The infrared image detail enhancement method according to claim 1, wherein The generating of the edge image includes: Subtract the base image from the filtered image to generate the edge image.

8. The infrared image detail enhancement method according to claim 1, characterized in that The generating of the 8-bit base image includes: Calculate the minimum pixel average value and the maximum pixel average value of the infrared image; Generate a compression range according to the minimum pixel average value and the maximum pixel average value; Calculate the minimum pixel value and the maximum pixel value of the first filtered image; Compress the contrast-enhanced image into an 8-bit base image according to the minimum pixel value, the maximum pixel value, and the compression range.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium for storing a computer program, characterized in that, When the computer program is executed by the processor, the method as described in any one of claims 1 to 8 is implemented.

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