CN110766639A - Image enhancement method and device, mobile equipment and computer readable storage medium - Google Patents
Image enhancement method and device, mobile equipment and computer readable storage medium Download PDFInfo
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Abstract
The invention provides an image enhancement method, an image enhancement device, mobile equipment and a computer readable storage medium, and relates to the technical field of data processing, wherein the method comprises the following steps: adjusting the brightness of an input image to be processed to obtain a brightness adjustment image; respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image; performing guiding filtering on the initial enhanced image according to the sharpened image to obtain a filtered image; and correcting the non-texture area of the filtered image according to the brightness adjusting image to obtain a target enhanced image. The method can be better suitable for more use scenes and has good generalization.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an image enhancement method and apparatus, a mobile device, and a computer-readable storage medium.
Background
With the development of the photographing function of a mobile device such as a mobile phone, the demand of a user for a camera module in the mobile device is increasing.
The HDR (High-Dynamic Range) image has a very uneven gray value distribution, and only a few pixels are bright, so that image enhancement needs to be performed on the HDR image. However, in order to work well in a mobile device with poor hardware performance, such as a mobile phone, the existing image enhancement method for the HDR image generally has some inherent defects, which results in fewer applicable use scenes and poor generalization.
Disclosure of Invention
In view of the above, the present invention provides an image enhancement method, an image enhancement apparatus, a mobile device and a computer-readable storage medium, so as to improve generalization.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, an embodiment of the present invention provides an image enhancement method, applied to a high dynamic range image, where the method includes: adjusting the brightness of an input image to be processed to obtain a brightness adjustment image; respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image; performing guiding filtering on the initial enhanced image according to the sharpened image to obtain a filtered image; and correcting the non-texture area of the filtering image according to the brightness adjusting image to obtain a target enhanced image.
Further, the step of adjusting the brightness of the input image to be processed to obtain a brightness-adjusted image includes: carrying out overexposure area detection on an input image to be processed to obtain an overexposure mask corresponding to the input image; the overexposure mask comprises an overexposure intensity value which corresponds to each pixel point of the input image one by one, and the overexposure intensity value is used for representing the overexposure degree of the corresponding pixel point; and adjusting the brightness of the input image according to a preset brightness adjustment parameter value and the overexposure mask to obtain a brightness adjustment image.
Further, the step of performing overexposure area detection on the input image to be processed to obtain an overexposure mask corresponding to the input image includes: each pixel point in the input image to be processed is processed as follows: comparing the pixel value of the pixel point with a preset high overexposure threshold value and a preset low overexposure threshold value respectively to obtain a comparison result; determining an overexposure value corresponding to the pixel point according to the comparison result; and generating an overexposure mask corresponding to the input image based on the overexposure intensity value of each pixel point.
Further, the step of adjusting the brightness of the input image according to a preset brightness parameter value and the overexposure mask to obtain a brightness adjusted image includes: adjusting the brightness of the input image through the following formula to obtain a brightness adjustment image:
x′i＝xi+xi×a×(1.0-yi)，
wherein, x'iRepresenting the pixel value, x, of a pixel point i in the brightness adjusted imageiRepresenting the pixel value of a pixel point i in the input image, a representing the brightness parameter value, yiAnd representing the overexposure intensity value corresponding to the pixel point i.
Further, the step of performing correction of a non-texture region on the filtered image according to the brightness adjustment image to obtain a target enhanced image includes: determining a non-textured region of the input image; performing brightness consistency optimization on the brightness adjusting image and a region corresponding to the non-texture region according to the filtering image to obtain a brightness optimized image; and replacing the region corresponding to the non-texture region of the filtering image according to the brightness optimized image to obtain a target enhanced image.
Further, the step of determining the non-texture region of the input image comprises: performing image edge detection on the input image to obtain an edge image marked with edge pixel points; carrying out rectangular block division on the edge image according to a preset division size to obtain a plurality of rectangular blocks; and counting the number of edge pixel points contained in each rectangular block, and judging whether the rectangular block belongs to a non-texture area or not according to the number of the edge pixel points contained in the rectangular block.
Further, the step of performing brightness consistency optimization on the brightness adjustment image and the region corresponding to the non-texture region according to the filtered image to obtain a brightness optimized image includes: respectively calculating the average brightness values of the brightness adjustment image and the filtering image in the area corresponding to the non-texture area to obtain a first brightness value corresponding to the brightness adjustment image and a second brightness value corresponding to the filtering image; determining a brightness parameter according to the first brightness value and the second brightness value, wherein the brightness parameter comprises a brightness ratio or a brightness difference; and adjusting the brightness of the region corresponding to the non-texture region in the brightness adjustment image according to the brightness parameter to obtain a brightness optimization image.
Further, the step of replacing the region corresponding to the non-texture region of the filtered image according to the brightness optimized image to obtain a target enhanced image includes: acquiring an initial fusion weight image corresponding to a non-texture region of the input image; the weight value corresponding to the non-texture area in the initial fusion weight image is 1, and the weight value corresponding to the texture area is 0; smoothing the initial fusion weight image to obtain a target fusion weight image; based on the target fusion weight image, fusing the brightness optimization image and the filtering image through the following formula to obtain a target enhanced image:
wherein the content of the first and second substances,
a pixel value representing a pixel point i in the target enhanced image,
representing the pixel value of a pixel point i in said filtered image,
representing the pixel value, k, of a pixel point i in the luminance-optimized imageiAnd representing the weight value of the pixel point i in the target fusion weight image.
Further, before the step of adjusting the brightness of the input image to be processed to obtain a brightness adjusted image, the method further includes: acquiring an original image; performing downsampling of preset multiples on the original image to obtain an input image to be processed;
after the step of performing correction of the non-texture region on the filtered image according to the brightness adjustment image to obtain a target enhanced image, the method further includes: determining an initial enhancement weight image according to the input image and the target enhancement image; performing upsampling on the initial enhancement weight image by the preset multiple to obtain a target enhancement weight image; and determining a final result image of the original image after image enhancement according to the target enhancement weight image.
In a second aspect, an embodiment of the present invention further provides an image enhancement apparatus applied to a high dynamic range image, where the apparatus includes: the adjusting module is used for adjusting the brightness of the input image to be processed to obtain a brightness adjusting image; the processing module is used for respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image; the filtering module is used for guiding and filtering the initial enhanced image according to the sharpened image to obtain a filtered image; and the correction module is used for correcting the non-texture area of the filtering image according to the brightness adjusting image to obtain a target enhanced image.
In a third aspect, an embodiment of the present invention provides a mobile device, including: a processor and a storage device; the storage device has stored thereon a computer program which, when executed by the processor, performs the method of any one of the above-provided first aspects.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, performs the steps of the method according to any one of the above-mentioned first aspect.
The embodiment of the invention provides an image enhancement method, an image enhancement device, mobile equipment and a computer readable storage medium, wherein when an HDR image is enhanced, firstly, brightness adjustment is carried out on an input image to be processed to obtain a brightness adjustment image; then, respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image, and carrying out guide filtering on the initial enhanced image according to the sharpened image to obtain a filtered image; and correcting the non-texture area of the filtered image according to the brightness adjusting image to obtain a target enhanced image. The method has no inherent defects in the prior art, and can be better suitable for more use scenes, so compared with the prior art, the image enhancement method provided by the embodiment improves the generalization.
Additional features and advantages of embodiments of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of the invention as set forth above.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 illustrates a schematic structural diagram of a mobile device according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating an image enhancement method provided by an embodiment of the invention;
FIG. 3 is a flow chart illustrating another image enhancement method provided by an embodiment of the present invention;
FIG. 4a illustrates an input image provided by an embodiment of the present invention;
FIG. 4b shows a luminance adjustment image corresponding to the input image shown in FIG. 4 a;
FIG. 4c shows the initial enhanced image corresponding to the brightness adjusted image shown in FIG. 4 b;
FIG. 4d illustrates a sharpened image corresponding to the brightness adjusted image illustrated in FIG. 4 b;
FIG. 4e shows a filtered image corresponding to the input image shown in FIG. 4 a;
FIG. 4f illustrates a corresponding non-textured area mask of the input image shown in FIG. 4 a;
FIG. 4g shows a target enhanced image corresponding to the input image shown in FIG. 4 a;
fig. 5 is a block diagram illustrating an image enhancement apparatus according to an embodiment of the present invention;
fig. 6 shows a block diagram of another image enhancement apparatus provided in the embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, not all, embodiments of the present invention.
Currently, HDR algorithms are usually adopted to improve the image quality of HDR images. The HDR algorithm can greatly improve the image quality of night scene shooting without adding extra hardware, and is very helpful for improving the camera experience of users. The HDR algorithm has been developed greatly in these years as an important field of image algorithms, and tone-mapping (tone mapping) is also proposed as an important step of the HDR algorithm. the tone-mapping algorithm is an algorithm for mapping and transforming image colors, and aims to adjust the gray level of an image, so that the processed image looks more comfortable to human eyes, and information and characteristics in the original image can be better expressed, namely the tone-mapping algorithm is used for realizing image enhancement of an HDR image. In order to achieve good operation in a mobile device with poor hardware performance, the complexity of an image enhancement method for HDR images cannot be too high, however, such a tone-mapping algorithm generally has some inherent defects, such as local histogram enhancement and the like, which are prone to generate strange block anomalies, so that the existing tone-mapping algorithm (image enhancement method) is less suitable for use scenes and has poor generalization.
In view of the poor generalization of the image enhancement method in the prior art, to improve the problem, the image enhancement method, the image enhancement device, the mobile device, and the computer-readable storage medium provided in embodiments of the present invention may be applied to any occasion where image enhancement processing needs to be performed on an HDR image, and may be directly applied by any mobile device having a photographing function of photographing an HDR image, such as a camera device, a mobile phone, a camera, and the like, through a respective processor during photographing, or may be applied by an intelligent terminal, such as a computer, after receiving an HDR image acquired by an image acquisition device, during performing post-image enhancement processing on the HDR image, and the following describes embodiments of the present invention in detail.
The first embodiment is as follows:
first, an example mobile device 100 for implementing an image enhancement method of an embodiment of the present invention is described with reference to fig. 1.
As shown in fig. 1, a mobile device 100 includes one or more processors 102, one or more memory devices 104, an input device 106, an output device 108, and an image capture device 110, which are interconnected via a bus system 112 and/or other type of connection mechanism (not shown). It should be noted that the components and structure of the mobile device 100 shown in fig. 1 are exemplary only, and not limiting, and the mobile device may have some of the components shown in fig. 1, or other components and structures not shown in fig. 1, as desired.
The processor 102 may be implemented in at least one hardware form of a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), the processor 102 may be one or a combination of several of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), or other forms of processing units having data processing capabilities and/or instruction execution capabilities, and may control other components in the mobile device 100 to perform desired functions.
The storage 104 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. On which one or more computer program instructions may be stored that may be executed by processor 102 to implement client-side functionality (implemented by the processor) and/or other desired functionality in embodiments of the invention described below. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer-readable storage medium.
The input device 106 may be a device used by a user to input instructions and may include one or more of a keyboard, a mouse, a microphone, a touch screen, and the like.
The output device 108 may output various information (e.g., images or sounds) to the outside (e.g., a user), and may include one or more of a display, a speaker, and the like.
The image capture device 110 may capture an image desired by a user (e.g., an HDR image, etc.) and store the captured image in the storage device 104 for use by other components.
Exemplarily, an exemplary mobile device for implementing the image enhancement method according to an embodiment of the present invention may be implemented as a smart terminal such as a smartphone, a tablet computer, a computer, or the like.
Example two:
referring to the flowchart of an image enhancement method shown in fig. 2, the method mainly includes the following steps S202 to S208:
step S202, brightness adjustment is carried out on the input image to be processed, and a brightness adjustment image is obtained.
The input image to be processed described above may be a high dynamic range image (HDR image) rich in details but poor in luminance dynamic range. The brightness of the pixel points (especially in the darker area) in the input image is improved by adjusting the brightness of the input image to be processed.
And step S204, respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image.
For example, a local contrast enhancement algorithm may be used to perform local contrast enhancement on the brightness-adjusted image to obtain an initial enhanced image; sharpening the brightness adjustment image by adopting a sharpening algorithm to obtain a sharpened image; the local Contrast enhancement algorithm may be, but is not Limited to, a CLAHE (Contrast Limited Adaptive histogram equalization) algorithm, and the sharpening algorithm may be, but is not Limited to, an USM (Unsharp Mask) sharpening algorithm. It should be noted that there is no sequential execution order between the local contrast enhancement processing and the sharpening processing, and the local contrast enhancement processing may be performed first, and then the sharpening processing may be performed; or sharpening is carried out first, and then local contrast enhancement is carried out.
And step S206, performing guiding filtering on the initial enhanced image according to the sharpened image to obtain a filtered image.
In specific implementation, the image may be sharpened as a guide image, and the initial enhanced image is subjected to guide filtering (guidefilter) by using an existing guide filtering algorithm to obtain a filtered image.
And S208, correcting the non-texture area of the filtered image according to the brightness adjusting image to obtain a target enhanced image.
Because the filtering image is obtained based on the initial enhanced image and the sharpened image, compared with the input image, a certain noise interference is inevitably introduced into the non-texture area of the filtering image, so that the brightness adjustment image can be used for correcting the non-texture area of the filtering image, and the enhancement effect of the target enhanced image can be ensured.
The image enhancement method provided by the embodiment can be better suitable for most of use scenes when the HDR image is subjected to image enhancement, and has good generalization; the method is low in complexity and can be well operated in mobile equipment such as a mobile phone.
In order to facilitate the user to adjust the brightness of the input image according to the preference of the user, when the brightness of the input image to be processed is adjusted in step S202 to obtain a brightness adjustment image, the following steps 1.1 and 1.2 may be referred to for execution:
step 1.1, carrying out overexposure area detection on an input image to be processed to obtain an overexposure mask corresponding to the input image; the overexposure mask comprises an overexposure intensity value which corresponds to each pixel point of the input image in a one-to-one mode, and the overexposure intensity value is used for representing the overexposure degree of the corresponding pixel point.
The overexposure mask can be understood as an overexposed image corresponding to the input image, and the pixel value of each pixel point in the overexposed image is the overexposure intensity value of the corresponding pixel point in the input image.
In some possible embodiments, the detection manner of the detection of the overexposed region in step 1 may be: each pixel point in the input image to be processed is processed as follows: comparing the pixel value of the pixel point with a preset high overexposure threshold value and a preset low overexposure threshold value respectively to obtain a comparison result; determining an overexposure value corresponding to the pixel point according to the comparison result; and generating an overexposure mask corresponding to the input image based on the overexposure intensity value of each pixel point. The high overexposure threshold and the low overexposure threshold are empirical values, and can be set according to actual requirements in specific implementation.
In a specific implementation manner, the step of determining the overexposure value corresponding to the pixel point according to the comparison result includes: when the comparison result is that the pixel value of the pixel point is greater than or equal to the high overexposure threshold value, the pixel point is considered to be completely overexposed, and the overexposure intensity value corresponding to the pixel point is determined to be 1; when the comparison result is that the pixel value of the pixel point is less than or equal to the low overexposure threshold value, the pixel point is considered not to be overexposed at all, and the overexposure intensity value corresponding to the pixel point is determined to be 0; when the comparison result is that the pixel value of the pixel point is greater than the low overexposure threshold and less than the high overexposure threshold, the overexposure value corresponding to the pixel point can be calculated by the following formula:
wherein, yiRepresenting the overexposure value, x, corresponding to the pixel point iiThe pixel value, x, of a pixel point iminIndicating a low overexposure threshold, xmaxIndicating a high overexposure threshold.
For example, the high overexposure threshold is preset to be 175, the low overexposure threshold is preset to be 128, and if the pixel value of a pixel is 140, the overexposure value corresponding to the pixel is:
and step 1.2, adjusting the brightness of the input image according to a preset brightness adjustment parameter value and the overexposure mask to obtain a brightness adjustment image.
In order to meet the requirement of the user on the personalized brightness preference, the embodiment provides a parameter-brightness adjustment parameter value which can be adjusted by the user, and the brightness adjustment parameter value can directly reflect the brightness level of the finally obtained image (target enhanced image). The image enhancement method provided by the embodiment facilitates parameter adjustment of the user, and can be adapted to different preferences of the user through a simple parameter, namely a brightness parameter value. The value of the brightness parameter may be set according to the preference of the user, for example, the value of the brightness parameter is 0.5.
In specific implementation, the brightness of the input image may be adjusted through the following formula to obtain a brightness-adjusted image:
x′i＝xi+xi×a×(1.0-yi)，
wherein, x'iRepresenting the pixel value, x, of a pixel point i in a luminance-adjusted imageiRepresenting the pixel value of a pixel point i in the input image, a representing the luminance parameter value, yiAnd representing the overexposure intensity value corresponding to the pixel point i.
Considering that when the non-texture region of the filtered image is directly replaced with the non-texture region of the brightness adjustment image, the overall brightness of the filtered image and the brightness adjustment image is not consistent, which may result in an unnatural transition between the non-texture region and the texture region of the obtained target enhanced image, when the non-texture region of the filtered image is corrected according to the brightness adjustment image in the step S208, so as to obtain the target enhanced image, the following steps 2.1 to 2.3 may be referred to for execution:
and 2.1, determining a non-texture area of the input image.
Texture is a visual feature that reflects the phenomenon of homogeneity in an image, and it represents the organized arrangement of surface structures on the surface of an object with slow or periodic variations. Texture is represented by the gray distribution of a pixel and its surrounding spatial neighborhood, i.e. local texture information. The local texture information is the global texture information with different degrees of repeatability. The regions of the image without texture features are non-texture regions.
In some possible embodiments, the non-textured region of the input image may be determined by: carrying out image edge detection on an input image to obtain an edge image marked with edge pixel points; dividing the edge image into rectangular blocks according to a preset division size to obtain a plurality of rectangular blocks; and for each rectangular block, counting the number of edge pixel points contained in the rectangular block, and judging whether the rectangular block belongs to a non-texture area or not according to the number of the edge pixel points contained in the rectangular block. The preset division size is an empirical value, and generally, the larger the size of the edge image is, the larger each rectangular block is divided.
In some possible implementations, an edgeMat image (edge image) can be obtained when image edge detection is performed on an input image by using, for example, a sobel operator, a laplacian operator, or a canny edge detection algorithm; then dividing the edge image into a plurality of rectangular blocks according to the division size such as 20 × 20 or 30 × 30, and calculating the number of edge pixel points in each rectangular block; then, for each rectangular block, whether the number of edge pixels in the rectangular block is greater than the product of the total number of pixels in the rectangular block and a preset percentage (such as 5%) or whether the ratio of the number of edge pixels in the rectangular block to the total number of pixels in the rectangular block is greater than the preset percentage is judged, and if not, the rectangular block is a non-texture area. The preset percentage is a tolerance value added to avoid noise interference, and the preset percentage belongs to an empirical value and can be adjusted according to actual needs. For example, the preset percentage is 5%, the counted number of edge pixels in a certain rectangular block a (the total number of pixels is 20 × 20 — 400) is 30, and it is determined that 30/400 is greater than 5%, so that the rectangular block a is not a non-texture region; for another example, the number of edge pixels in the rectangular block B (the total number of pixels is 20 × 20 — 400) is 10, and 10/400 is determined to be less than 5%, so the rectangular block B is a non-texture region.
In specific implementation, by determining whether each rectangular block is a non-texture region, a non-texture region mask corresponding to the input image may be obtained, and the non-texture region mask may be understood as an image that distinguishes the non-texture region from the texture region. The pixel values of the pixels in the non-texture region in the mask of the non-texture region are all marked as 1, and the pixel values of the pixels in the texture region are all marked as 0.
And 2.2, performing brightness consistency optimization on the brightness adjusting image and a region corresponding to the non-texture region of the input image according to the filtered image to obtain a brightness optimized image.
The non-texture region of the brightness adjustment image (the region corresponding to the non-texture region of the input image) and the non-texture region of the filter image (the region corresponding to the non-texture region of the input image) can be determined through the execution result of the step 2.1, and the overall brightness of the non-texture region of the brightness adjustment image is kept consistent with the overall brightness of the non-texture region of the filter image through the brightness consistency optimization of the non-texture region, so that the subsequently obtained target enhanced image is more natural in overall brightness.
In some possible embodiments, the brightness consistency optimization of the brightness adjustment image with the area corresponding to the non-texture area of the input image may be performed by the following process: respectively calculating the average brightness values of the brightness adjustment image and the filtering image in the area corresponding to the non-texture area of the input image to obtain a first brightness value corresponding to the brightness adjustment image and a second brightness value corresponding to the filtering image; determining a brightness parameter according to the first brightness value and the second brightness value, wherein the brightness parameter comprises a brightness ratio or a brightness difference; and adjusting the brightness of the area corresponding to the non-texture area of the input image in the brightness adjustment image according to the brightness parameter to obtain a brightness optimized image.
During specific implementation, the average brightness values of the regions marked as 1 in the corresponding non-texture region masks of the brightness adjustment image and the filtering image are respectively calculated, and the average brightness value is the average value of the pixel values of corresponding pixel points; the following mode 1 or mode 2 is then performed:
mode 1: dividing the second brightness value corresponding to the filtering image by the first brightness value corresponding to the brightness adjusting image to obtain a brightness ratio; and multiplying the brightness of each pixel point of the non-texture area in the brightness adjustment image by the brightness ratio to obtain a brightness optimization image.
Mode 2: subtracting the first brightness value corresponding to the brightness adjustment image from the second brightness value corresponding to the filtering image to obtain an overall brightness difference (brightness difference value); and adding the brightness of each pixel point of the non-texture area in the brightness adjustment image to the overall brightness difference to obtain a brightness optimization image.
And 2.3, replacing the corresponding area of the non-texture area of the input image with the filtering image according to the brightness optimized image to obtain a target enhanced image.
The non-texture region of the filtered image can be replaced by the non-texture region of the brightness optimized image, and the obtained target enhanced image is a fused image of the filtered image and the brightness optimized image.
In some optional embodiments, in order to ensure a smooth transition between a texture region and a non-texture region in the target enhanced image, the target enhanced image may be obtained by the following processes: firstly, acquiring an initial fusion weight image corresponding to a non-texture area of an input image; the weight value corresponding to the non-texture area in the initial fusion weight image is 1, and the weight value corresponding to the texture area is 0; then, smoothing the initial fusion weight image to obtain a target fusion weight image; based on the target fusion weight image, fusing the brightness optimization image and the filtering image through the following formula to obtain a target enhanced image:
wherein the content of the first and second substances,
the pixel value representing pixel point i in the target enhanced image,
representing the pixel value of a pixel point i in the filtered image,
representing the pixel value, k, of a pixel point i in a luminance-optimized imageiAnd representing the weight value of the pixel point i in the target fusion weight image.
In specific implementation, the obtained non-texture region mask can be determined as an initial fusion weight image, and the pixel value of each pixel point in the non-texture region mask is the weight of the corresponding pixel point in the initial fusion weight image; the initial fusion weight image can be smoothed by gaussian filtering to obtain a target fusion weight image. The target fusion weight image has natural transition between the texture area and the non-texture area through smooth processing, and the weighted value of the pixel point of the corresponding area has natural transition between 0 and 1 (for example, weighted values of 0.3, 0.5, 0.8 and the like exist), so that smooth transition of a subsequently obtained target enhanced image between the texture area and the non-texture area is ensured, and a boundary line or a strange boundary is prevented.
In addition, in order to further reduce the computation complexity and the computation amount, before the step of performing brightness adjustment on the input image to be processed to obtain a brightness adjustment image, the image enhancement method provided by this embodiment further includes: acquiring an original image; and carrying out downsampling on the original image by preset times to obtain an input image to be processed. After the step of performing non-texture region correction on the filtered image according to the input image and the brightness adjustment image to obtain the target enhanced image, the method provided by this embodiment further includes: determining an initial enhancement weight image according to the input image and the target enhancement image; performing upsampling on the initial enhancement weight image by the preset multiple to obtain a target enhancement weight image; and determining a final result image of the original image after image enhancement according to the target enhancement weight image. The preset multiple may be set according to actual requirements, and is not limited herein, for example, the preset multiple is 10 times or 8 times.
Therefore, the original image is firstly reduced, the target enhanced image of the reduced image (input image) is obtained, then the target enhanced image is mapped to the original image, and the final result image is obtained.
Example three:
on the basis of the foregoing embodiment, the present embodiment provides a specific example to which the foregoing image enhancement method is applied. Referring to the flowchart of another image enhancement method shown in fig. 3, the method mainly includes the following steps S302 to S312:
in step S302, an input image of HDR is acquired. An input image of HDR means that the input image is an HDR image.
Step S304, adjusting the brightness of the input image based on the preset brightness adjustment parameter value to obtain a brightness adjustment image.
And S306, respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image.
And step S308, taking the sharpened image as a guide image, and performing guide filtering on the initial enhanced image to obtain a filtered image.
Step S310, non-texture region detection is performed on the input image.
Step S312, using the non-texture region detection result of the input image to correct the non-texture region of the filtering image, so as to obtain the target enhanced image.
The image enhancement method provided by the embodiment can be better suitable for most of use scenes, and has good generalization; the method has low complexity and can be well operated in mobile equipment such as a mobile phone; in addition, the method is convenient for users to adjust parameters.
In order to verify the effectiveness of the image enhancement method, the present embodiment performs image enhancement verification on one input image shown in fig. 4a, and the verification result is as follows: fig. 4b shows a luminance adjustment image corresponding to the input image shown in fig. 4a, fig. 4c shows an initial enhanced image and fig. 4d shows a sharpened image corresponding to the luminance adjustment image shown in fig. 4b, fig. 4e shows a filtered image obtained based on fig. 4c and fig. 4d, fig. 4f shows a non-texture area mask corresponding to the input image shown in fig. 4a, and fig. 4g shows a final target enhanced image. As can be seen from fig. 4g, compared with the input image, the overall brightness of the target enhanced image is significantly improved, the transition of the target enhanced image between the non-texture region and the texture region is natural, and the image enhancement effect is better.
Example four:
as to the image enhancement method provided in the second embodiment, an embodiment of the present invention provides an image enhancement apparatus, which is applied to an HDR image, and referring to a block diagram of a structure of an image enhancement apparatus shown in fig. 5, the apparatus includes the following modules:
the adjusting module 52 is configured to perform brightness adjustment on the input image to be processed to obtain a brightness adjustment image;
the processing module 54 is configured to perform contrast enhancement processing and sharpening processing on the brightness-adjusted image respectively to obtain an initial enhanced image and a sharpened image;
the filtering module 56 is configured to perform guided filtering on the initial enhanced image according to the sharpened image to obtain a filtered image;
and the correcting module 58 is configured to correct the non-texture region of the filtered image according to the brightness adjustment image, so as to obtain a target enhanced image.
The image enhancement device provided by the embodiment can be better suitable for more use scenes, and has good generalization.
In an embodiment, the adjusting module 52 is specifically configured to:
carrying out overexposure area detection on an input image to be processed to obtain an overexposure mask corresponding to the input image; the overexposure mask comprises an overexposure intensity value which corresponds to each pixel point of the input image one by one, and the overexposure intensity value is used for representing the overexposure degree of the corresponding pixel point;
and adjusting the brightness of the input image according to the preset brightness adjustment parameter value and the overexposure mask to obtain a brightness adjustment image.
Further, the adjusting module 52 is further configured to:
each pixel point in the input image to be processed is processed as follows: comparing the pixel value of the pixel point with a preset high overexposure threshold value and a preset low overexposure threshold value respectively to obtain a comparison result; determining an overexposure value corresponding to the pixel point according to the comparison result;
and generating an overexposure mask corresponding to the input image based on the overexposure intensity value of each pixel point.
Further, the adjusting module 52 is further configured to:
the brightness adjustment is carried out on the input image through the following formula to obtain a brightness adjustment image:
x′i＝xi+xi×a×(1.0-yi)，
wherein, x'iRepresenting the pixel value, x, of a pixel point i in a luminance-adjusted imageiRepresenting the pixel value of a pixel point i in the input image, a representing the luminance parameter value, yiAnd representing the overexposure intensity value corresponding to the pixel point i.
In one embodiment, the modification module 58 is specifically configured to:
determining a non-texture region of an input image;
performing brightness consistency optimization on the brightness adjusting image and the region corresponding to the non-texture region according to the filtered image to obtain a brightness optimized image;
and replacing the region corresponding to the non-texture region of the filtered image according to the brightness optimized image to obtain a target enhanced image.
Further, the modification module 58 is further configured to:
carrying out image edge detection on an input image to obtain an edge image marked with edge pixel points;
dividing the edge image into rectangular blocks according to a preset division size to obtain a plurality of rectangular blocks;
and for each rectangular block, counting the number of edge pixel points contained in the rectangular block, and judging whether the rectangular block belongs to a non-texture area or not according to the number of the edge pixel points contained in the rectangular block.
Further, the modification module 58 is further configured to:
respectively calculating the average brightness values of the brightness adjustment image and the filtering image in the area corresponding to the non-texture area to obtain a first brightness value corresponding to the brightness adjustment image and a second brightness value corresponding to the filtering image;
determining a brightness parameter according to the first brightness value and the second brightness value, wherein the brightness parameter comprises a brightness ratio or a brightness difference;
and adjusting the brightness of the region corresponding to the non-texture region in the brightness adjustment image according to the brightness parameter to obtain a brightness optimization image.
Further, the modification module 58 is further configured to:
acquiring an initial fusion weight image corresponding to a non-texture region of an input image; the weight value corresponding to the non-texture area in the initial fusion weight image is 1, and the weight value corresponding to the texture area is 0;
smoothing the initial fusion weight image to obtain a target fusion weight image;
based on the target fusion weight image, fusing the brightness optimization image and the filtering image through the following formula to obtain a target enhanced image:
wherein the content of the first and second substances,
the pixel value representing pixel point i in the target enhanced image,
representing the pixel value of a pixel point i in the filtered image,
representing the pixel value, k, of a pixel point i in a luminance-optimized imageiAnd representing the weight value of the pixel point i in the target fusion weight image.
In an embodiment, referring to a block diagram of another image enhancement apparatus shown in fig. 6, on the basis of fig. 5, the apparatus further includes:
an obtaining module 62, configured to obtain an original image; the method comprises the steps of conducting downsampling on an original image by preset times to obtain an input image to be processed;
a mapping module 64 for determining an initial enhancement weight image from the input image and the target enhancement image; performing up-sampling on the initial enhancement weight image by a preset multiple to obtain a target enhancement weight image; and determining a final result image of the original image after image enhancement according to the target enhancement weight image.
The device provided by the embodiment has the same implementation principle and technical effect as the foregoing embodiment, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiment for the portion of the embodiment of the device that is not mentioned.
In addition, the present embodiment further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method provided in the second embodiment.
The image enhancement method, the image enhancement device, the mobile device, and the computer program product of the computer-readable storage medium according to the embodiments of the present invention include a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute 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 removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (12)
1. An image enhancement method applied to a high dynamic range image, the method comprising:
adjusting the brightness of an input image to be processed to obtain a brightness adjustment image;
respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image;
performing guiding filtering on the initial enhanced image according to the sharpened image to obtain a filtered image;
and correcting the non-texture area of the filtering image according to the brightness adjusting image to obtain a target enhanced image.
2. The method according to claim 1, wherein the step of adjusting the brightness of the input image to be processed to obtain a brightness-adjusted image comprises:
carrying out overexposure area detection on an input image to be processed to obtain an overexposure mask corresponding to the input image; the overexposure mask comprises an overexposure intensity value which corresponds to each pixel point of the input image one by one, and the overexposure intensity value is used for representing the overexposure degree of the corresponding pixel point;
and adjusting the brightness of the input image according to a preset brightness adjustment parameter value and the overexposure mask to obtain a brightness adjustment image.
3. The method according to claim 2, wherein the step of performing overexposure area detection on the input image to be processed to obtain an overexposure mask corresponding to the input image comprises:
each pixel point in the input image to be processed is processed as follows: comparing the pixel value of the pixel point with a preset high overexposure threshold value and a preset low overexposure threshold value respectively to obtain a comparison result; determining an overexposure value corresponding to the pixel point according to the comparison result;
and generating an overexposure mask corresponding to the input image based on the overexposure intensity value of each pixel point.
4. The method according to claim 2, wherein the step of performing luminance adjustment on the input image according to a preset luminance parameter value and the overexposure mask to obtain a luminance-adjusted image comprises:
adjusting the brightness of the input image through the following formula to obtain a brightness adjustment image:
x′i＝xi+xi×a×(1.0-yi)，
wherein, x'iRepresenting the pixel value, x, of a pixel point i in the brightness adjusted imageiRepresenting the pixel value of a pixel point i in the input image, a representing the brightness parameter value, yiAnd representing the overexposure intensity value corresponding to the pixel point i.
5. The method according to claim 1, wherein the step of performing the correction of the non-texture region on the filtered image according to the brightness adjustment image to obtain the target enhanced image comprises:
determining a non-textured region of the input image;
performing brightness consistency optimization on the brightness adjusting image and a region corresponding to the non-texture region according to the filtering image to obtain a brightness optimized image;
and replacing the region corresponding to the non-texture region of the filtering image according to the brightness optimized image to obtain a target enhanced image.
6. The method of claim 5, wherein the step of determining the non-textured region of the input image comprises:
performing image edge detection on the input image to obtain an edge image marked with edge pixel points;
carrying out rectangular block division on the edge image according to a preset division size to obtain a plurality of rectangular blocks;
and counting the number of edge pixel points contained in each rectangular block, and judging whether the rectangular block belongs to a non-texture area or not according to the number of the edge pixel points contained in the rectangular block.
7. The method according to claim 5, wherein the step of performing brightness consistency optimization on the brightness adjustment image and the region corresponding to the non-texture region according to the filtered image to obtain a brightness optimized image comprises:
respectively calculating the average brightness values of the brightness adjustment image and the filtering image in the area corresponding to the non-texture area to obtain a first brightness value corresponding to the brightness adjustment image and a second brightness value corresponding to the filtering image;
determining a brightness parameter according to the first brightness value and the second brightness value, wherein the brightness parameter comprises a brightness ratio or a brightness difference;
and adjusting the brightness of the region corresponding to the non-texture region in the brightness adjustment image according to the brightness parameter to obtain a brightness optimization image.
8. The method according to claim 5, wherein the step of replacing the region corresponding to the non-texture region of the filtered image according to the brightness-optimized image to obtain the target enhanced image comprises:
acquiring an initial fusion weight image corresponding to a non-texture region of the input image; the weight value corresponding to the non-texture area in the initial fusion weight image is 1, and the weight value corresponding to the texture area is 0;
smoothing the initial fusion weight image to obtain a target fusion weight image;
based on the target fusion weight image, fusing the brightness optimization image and the filtering image through the following formula to obtain a target enhanced image:
wherein the content of the first and second substances,
a pixel value representing a pixel point i in the target enhanced image,
representing the pixel value of a pixel point i in said filtered image,
representing the pixel value, k, of a pixel point i in the luminance-optimized imageiAnd representing the weight value of the pixel point i in the target fusion weight image.
9. The method according to any one of claims 1 to 8, wherein before the step of performing brightness adjustment on the input image to be processed to obtain a brightness-adjusted image, the method further comprises: acquiring an original image; performing downsampling of preset multiples on the original image to obtain an input image to be processed;
after the step of performing correction of the non-texture region on the filtered image according to the brightness adjustment image to obtain a target enhanced image, the method further includes:
determining an initial enhancement weight image according to the input image and the target enhancement image;
performing upsampling on the initial enhancement weight image by the preset multiple to obtain a target enhancement weight image;
and determining a final result image of the original image after image enhancement according to the target enhancement weight image.
10. An image enhancement apparatus, applied to a high dynamic range image, the apparatus comprising:
the adjusting module is used for adjusting the brightness of the input image to be processed to obtain a brightness adjusting image;
the processing module is used for respectively carrying out contrast enhancement processing and sharpening processing on the brightness adjustment image to obtain an initial enhanced image and a sharpened image;
the filtering module is used for guiding and filtering the initial enhanced image according to the sharpened image to obtain a filtered image;
and the correction module is used for correcting the non-texture area of the filtering image according to the brightness adjusting image to obtain a target enhanced image.
11. A mobile device, comprising: a processor and a storage device;
the storage device has stored thereon a computer program which, when executed by the processor, performs the method of any of claims 1 to 9.
12. A computer-readable storage medium, having a computer program stored thereon, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 9.
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