CN115880160A - Image processing method and device - Google Patents
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- CN115880160A CN115880160A CN202111131768.9A CN202111131768A CN115880160A CN 115880160 A CN115880160 A CN 115880160A CN 202111131768 A CN202111131768 A CN 202111131768A CN 115880160 A CN115880160 A CN 115880160A
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Abstract
The invention provides an image processing method and device. The method comprises the following steps: determining a weight image according to the brightness image with the compressed dynamic range, and carrying out Gaussian pyramid decomposition on the weight image; performing Laplacian pyramid decomposition on the luminance image; determining a target image pyramid according to the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition; and determining a brightness output image according to the target image pyramid, and recovering the color of the brightness output image. The image processing method and the image processing device provided by the invention can keep the contrast and the color information of the image while compressing the dynamic range of the HDR image, can well reproduce the image details, effectively solves the halo phenomenon which is difficult to solve by local tone mapping, can display the processed image on the traditional equipment, and meets the daily visual requirements of users.
Description
Technical Field
The invention relates to the technical field of image processing, frequency domain fusion and tone mapping, in particular to an image processing method and device.
Background
Currently, a High Dynamic Range (HDR) image can present brightness information and texture information of an extremely bright scene and an extremely dark scene in real life to people, and a general image does not have such characteristics. The brightness level of the image can reach 10 due to the high dynamic range 8 Whereas the brightness level of a normal display device is only 10 2 Such disparity in brightness levels makes HDR images impossible to display normally on ordinary display devices. Therefore, there is a need for tone mapping high dynamic range images for better display on legacy devices. The existing tone mapping methods mainly include a global tone mapping method and a local tone mapping method.
The global tone mapping method can perform the same operation on all pixel points in the HDR image, the overall contrast of the processed image is kept good, but the local details of the image are seriously lost, the overall processing effect is poor, and the common method is a Reinhard method.
The local tone mapping method adopts different mappings to different areas of the HDR image, the details of the processed image are well preserved, but the calculation complexity is high, the processed image is easy to generate a halo phenomenon, the overall quality of the image is affected, and common methods include frequency domain methods, such as a Durand method and a Rahma method.
Disclosure of Invention
The invention provides an image processing method and an image processing device, which are used for solving the technical problem of how to keep the overall contrast and the local details of an image in tone mapping.
In a first aspect, the present invention provides an image processing method, including:
determining a weight image according to the brightness image after the dynamic range is compressed, and carrying out Gaussian pyramid decomposition on the weight image;
performing Laplacian pyramid decomposition on the brightness image;
determining a target image pyramid according to the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition;
and determining a brightness output image according to the target image pyramid, and recovering the color of the brightness output image.
In one embodiment, before determining a weighted image according to the luminance image after compressing the dynamic range and performing gaussian pyramid decomposition on the weighted image, the method includes:
separating out the brightness component of the high dynamic range image;
determining a luminance image of the high dynamic range image from the luminance component.
In one embodiment, the compressed dynamic range luminance image is determined by:
and compressing the brightness image of the high dynamic range image according to a global tone mapping algorithm and a gamma correction algorithm respectively, and determining the brightness image after the dynamic range is compressed.
In one embodiment, the determining a weighted image according to the luminance image after compressing the dynamic range includes:
and determining the weight image according to the contrast, the exposure goodness and the gradient value of the brightness image after the dynamic range is compressed.
In one embodiment, the performing gaussian pyramid decomposition on the weighted image comprises:
and carrying out normalization processing on the weight image, and carrying out Gaussian pyramid decomposition on the weight image after the normalization processing.
In one embodiment, the determining a target image pyramid from the gaussian pyramid decomposed image and the laplacian pyramid decomposed image includes:
and multiplying the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition according to pixel distribution to obtain a target image pyramid.
In one embodiment, the determining a luminance output image according to the target image pyramid comprises:
performing Laplacian pyramid reconstruction on the target image pyramid;
and determining the brightness output image according to the reconstructed Laplacian pyramid.
In a second aspect, the present invention provides an image processing apparatus comprising:
the first determining module is used for determining a weight image according to the brightness image after the dynamic range is compressed and carrying out Gaussian pyramid decomposition on the weight image;
the decomposition module is used for carrying out Laplacian pyramid decomposition on the brightness image;
the second determining module is used for determining a target image pyramid according to the image subjected to the decomposition of the Gaussian pyramid and the image subjected to the decomposition of the Laplacian pyramid;
and the third determining module is used for determining a brightness output image according to the target image pyramid and recovering the color of the brightness output image.
In a third aspect, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the image processing method according to the first aspect when executing the computer program.
In a fourth aspect, the present invention provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the image processing method of the first aspect.
The image processing method, the image processing device, the electronic equipment and the storage medium provided by the invention have the advantages that the dynamic range of the HDR image can be obviously compressed through the decomposition and fusion of the double pyramids, the contrast and the color information of the image are kept, the overall effect is better, the image details can be well reproduced, the halo phenomenon which is difficult to solve by local tone mapping is effectively solved, the processed image can be displayed on the traditional equipment, and the daily visual requirement of a user is met.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of 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 is a schematic flow chart of an image processing method provided by the present invention;
FIG. 2 is a flow chart of an image processing method applying the present invention;
FIG. 3 is a schematic structural diagram of an image processing apparatus provided in the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of an image processing method provided by the present invention. Referring to fig. 1, an image processing method provided by the present invention may include:
s110, determining a weight image according to the brightness image with the compressed dynamic range, and performing Gaussian pyramid decomposition on the weight image;
s120, performing Laplacian pyramid decomposition on the brightness image;
s130, determining a target image pyramid according to the image subjected to the decomposition of the Gaussian pyramid and the image subjected to the decomposition of the Laplacian pyramid;
and S140, determining a brightness output image according to the target image pyramid, and recovering the color of the brightness output image.
It should be noted that the execution subject of the device information presentation method provided by the present invention may be an electronic device, a component in an electronic device, an integrated circuit, or a chip. The electronic device may be a mobile electronic device or a non-mobile electronic device. By way of example, the mobile electronic device may be a mobile phone, a tablet computer, a notebook computer, a palm top computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a Personal Digital Assistant (PDA), and the like, and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a Personal Computer (PC), a Television (TV), a teller machine or a self-service machine, and the like, and the present invention is not particularly limited.
Specifically, in step S110, a weight image is determined from the luminance image after the dynamic range is compressed, and gaussian pyramid decomposition is performed on the weight image. HDR images can present brightness information and texture information of extremely bright scenes and extremely dark scenes in real life to people, while general images do not have such characteristics. The brightness level of HDR image can reach 10 8 Whereas the brightness level of a normal display device is only 10 2 The disparity in brightness levels makes HDR images impossible to display normally on ordinary display devices. Therefore, there is a need for tone mapping of high dynamic range images for better display on conventional devices. Firstly, for an HDR image, determining a weight image according to a luminance image with a compressed dynamic range of the HDR image, and performing Gaussian pyramid decomposition on the weight image.
In step S120, laplacian pyramid decomposition is performed on the luminance image.
In step S130, the image after gaussian pyramid decomposition and the image after laplacian pyramid decomposition are fused to obtain a target image pyramid.
In step S140, a final luminance output image is determined according to the target image pyramid, and since the luminance image only includes the luminance of the image, the color of the luminance output image needs to be recovered at the same time.
The image processing method provided by the invention can obviously compress the dynamic range of the HDR image through decomposition and fusion of the double pyramids, simultaneously keeps the contrast and color information of the image, has better overall effect, can well reproduce the image details, effectively solves the halo phenomenon which is difficult to solve by local tone mapping, can display the processed image on the traditional equipment, and meets the daily visual requirements of users.
In one embodiment, before determining a weighted image according to the luminance image after compressing the dynamic range and performing gaussian pyramid decomposition on the weighted image, the method includes:
separating out the brightness component of the high dynamic range image;
a luminance image of the high dynamic range image is determined from the luminance component.
Specifically, the HDR image is converted from the RGB space to the CIEXYZ space, because the correlation of three components of X, Y, and Z in the CIEXYZ space is very small, so that the chromaticity of the image is not greatly affected when the dynamic range of luminance is compressed. Wherein, X, Z represent the chroma of the image, the luminance component is only related to Y, the conversion formula is as follows:
after the luminance component of the high dynamic range image is separated, the luminance image of the high dynamic range image can be determined from the luminance component.
The image processing method provided by the invention can determine the brightness image of the high dynamic range image according to the brightness component by separating the brightness component of the high dynamic range image, and can ensure that the chromaticity of the image is not greatly influenced when the dynamic range of the brightness is compressed.
In one embodiment, the luminance image after compressing the dynamic range is determined by:
and compressing the brightness image of the high dynamic range image according to a global tone mapping algorithm and a gamma correction algorithm respectively, and determining the brightness image after the dynamic range is compressed.
In particular, contrast can be maintained using Reinhard global TMO (harmonic mapping process). First, the average value of the image brightness Y in the logarithmic domain is calculated
All brightness is averaged logarithmically->
Normalized, the formula is as follows:
wherein L is w (x, y) is the luminance value at pixel point (x, y) in the HDR scene; n is the total number of pixels; δ is a small positive number, which ensures that the true number of the logarithmic function is not zero, L (x, y) is the luminance after compression, a is the luminance proportionality constant, usually 0.18, whose size determines the luminance of the output image.
Then, the image is linearly compressed, and the brightness value of the display device is L d (x, y) is expressed as:
all luminances are compressed between 0, 1), being normalized luminances. Then introducing L white Extending the function to a controllable function:
wherein L is white Refers to the minimum luminance value that is mapped to pure white. After the HDR image is processed by utilizing the Reinhard global TMO, most information of the image can be displayed, and a mapping result contains certain detail information. Therefore, the method can be used as a base image for post-processing.
The gamma correction algorithm includes: gamma correction algorithms with gamma greater than 1 and gamma less than 1. The use of gamma correction TMO can enhance detail. The gamma correction function is essentially a power function, is an increasing function in a first quadrant of a two-dimensional coordinate, and has different opening directions according to different values of powers, when the value of the power is less than 1, the opening of a function curve is downward, when the value of the power is greater than 1, the opening of the function curve is upward, and when the value of the power is equal to 1, the function image is a straight line.
For an image with brightness I, the formula of the gamma transformation is:
f(I)＝I γ
when γ < 1, the function changes faster when the argument inputs a value in a smaller range. Therefore, gamma < 1 is suitable for improving the image brightness and contrast of the low light area, highlighting the detail information of the low light area, improving the distortion of the image picture and being used as a basic image for post-processing.
When γ > 1, the function changes faster when the argument enters a value over a larger range. Therefore, gamma > 1 is suitable for improving the dynamic range of the high-illumination area and recovering the details of the image of the overexposed area, and can also be used as a basic graph for post-processing.
The entire effect of the image is ensured by respectively applying a Reinhard global tone mapping algorithm, the details of an over-exposure area of the image are restored by a gamma correction algorithm with the gamma larger than 1, the brightness information and the contrast information of a dark area of the image are improved by a gamma correction algorithm with the gamma smaller than 1, and three compressed brightness images can be obtained after three complementary tone mapping algorithms are adopted for processing.
According to the image processing method provided by the invention, the whole image effect is ensured by applying the Reinhard global tone mapping algorithm, the gamma correction algorithm with the gamma larger than 1 recovers the details of the over-exposure area of the image, the gamma correction algorithm with the gamma smaller than 1 improves the brightness information and the contrast information of the dark area of the image, the three mapping methods are poor in image effect when used independently and have complementarity when combined, and after the three tone mapping algorithms with complementarity are used for processing, each area of an HDR image can be fully compressed and the image details are supplemented.
In one embodiment, determining a weighted image from the luminance image after compressing the dynamic range comprises:
and determining a weight image according to the contrast, the exposure goodness and the gradient value of the brightness image after the dynamic range is compressed.
Specifically, a laplacian filter is usually applied to filter three input different luminance images, and the absolute value of the filtered result is taken to generate an edge characteristic factor C, where a standard laplacian template is defined as:
the method for calculating the contrast characteristic of the image comprises the following steps:
C k ＝I k *h
wherein, C k Is a given k-th image I k Convolution with the template h, i.e. the contrast value of the k-th image.
The intensity value of an image shows the degree of exposure of each pixel, and since the image has been normalized before the quality measure is calculated, a gaussian function is usually used
The brightness of the image is measured based on a curve of 0.5, and the value of sigma is usually 0.2, so that the calculated value is the exposure quality of the imageAnd (5) the goodness.
The scheme adopts a first-order Sobel operator to describe the edge information of the image, and the operator performs convolution on the original brightness image by utilizing two given 3 multiplied by 3 matrixes to obtain a transverse gradient G x And a longitudinal gradient G y Then G of each pixel in the image x And G y And combining to obtain the gradient value G of the whole image, wherein the gradient value G can directly reflect the image information contained in the image edge. The specific calculation process is as follows:
the contrast, the exposure goodness and the gradient value of each brightness image are calculated and multiplied respectively to obtain a weight image, namely the weight image of each brightness image can be represented by the product combination of three quality measure factors.
The contrast, the exposure goodness and the gradient value are used as quality measure factors, and when the images are decomposed in a multi-scale mode, information with different emphasis of each image can be combined, so that the finally processed images can better meet the visual requirements of users at the edge protruding parts.
According to the image processing method provided by the invention, the image contrast, the exposure saturation and the gradient value of the luminance image after tone mapping are extracted as quality measure factors, so that the information of each image can be combined, and the definition and the image detail information of the final fusion image at the edge protruding part are improved.
In one embodiment, the gaussian pyramid decomposition of the weighted image comprises:
and carrying out normalization processing on the weight image, and carrying out Gaussian pyramid decomposition on the weight image after the normalization processing.
Specifically, the weighted image is subjected to normalization processing, and the image normalization is a process of performing a series of standard processing transformations on the image to transform the image into a fixed standard form. The original image can obtain various duplicate images after being subjected to some processing or attack, the images can obtain standard images in the same form after being subjected to image normalization processing with the same parameters, and subsequent calculation amount can be greatly reduced.
According to the image processing method provided by the invention, the standard images in the same form can be obtained by normalizing the weight images, and the calculated amount in the image processing process can be greatly reduced.
In one embodiment, determining a target image pyramid from the gaussian pyramid decomposed image and the laplacian pyramid decomposed image comprises:
and multiplying the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition according to pixel distribution to obtain a target image pyramid.
Specifically, the image after the gaussian pyramid decomposition and the image after the laplacian pyramid decomposition are multiplied by pixel distribution for fusion, so that a target image pyramid can be obtained.
According to the image processing method provided by the invention, the images after two decompositions are multiplied by pixel distribution for fusion to obtain the target image pyramid, the brightness output image can be determined according to the target image pyramid, the color of the brightness output image is recovered, the dynamic range of the HDR image is compressed, the contrast and the color information of the image can be kept, the image details can be well reproduced, the halo phenomenon which is difficult to solve by local tone mapping is effectively solved, the processed image can be displayed on the traditional equipment, and the daily visual requirement of a user is met.
In one embodiment, determining a luminance output image from the target image pyramid comprises:
carrying out Laplacian pyramid reconstruction on the target image pyramid;
and determining a brightness output image according to the reconstructed Laplacian pyramid.
Specifically, corresponding layers of the target image pyramid are added according to pixels, laplacian pyramid reconstruction is performed, a fused brightness output image is obtained, the brightness output image is converted into an RGB space from a CIEXYZ space, and the color of the image is restored.
According to the image processing method provided by the invention, the target image pyramid is subjected to Laplacian pyramid reconstruction, and the brightness output image is determined according to the reconstructed Laplacian pyramid, so that the chromaticity of the image can be ensured not to be greatly influenced when the dynamic range of the brightness is compressed.
The following describes the technical solution provided by the present invention by taking a flowchart of an image processing method provided by the present invention as an example, and fig. 2 is a flowchart of an image processing method provided by the present invention.
First, the luminance component of the high dynamic range image is separated, and then the luminance image of the high dynamic range image is determined according to the luminance component.
The method comprises the steps of respectively using a Reinhard global tone mapping algorithm to ensure the overall effect of an image, restoring the details of an over-exposure area of the image by using a gamma correction algorithm with the gamma larger than 1, improving the brightness information and the contrast information of a dark area of the image by using a gamma correction algorithm with the gamma smaller than 1, and obtaining three compressed brightness images after processing by using three tone mapping algorithms with complementarity.
And respectively calculating the contrast, the exposure goodness and the gradient value of the three compressed brightness images as three different quality measure factors.
And multiplying the three quality measure factors of each compressed brightness image to obtain a weight image.
And carrying out normalization processing on the weight image, and then carrying out Gaussian pyramid decomposition on the weight image.
And performing Laplacian pyramid decomposition on the three compressed brightness images.
Multiplying the images after the pyramid decomposition twice according to pixel distribution to obtain a target pyramid image;
and performing Laplacian pyramid reconstruction on the target pyramid image to obtain a final brightness output image.
And recovering the color of the brightness output image to obtain a finally processed image.
The image processing method provided by the invention can combine the information of each image when performing multi-scale and multi-resolution decomposition on the image, thereby improving the definition of the final fused image at the edge salient part and the image detail information.
In addition, the dual-pyramid frequency domain fusion TMO method and the dual-pyramid frequency domain fusion TMO step can extract image contrast, exposure saturation and gradient values of the luminance image subjected to tone mapping as quality measure factors, adopt a Gaussian pyramid to process a weighted image obtained by multiplying the three quality measure factors, adopt a Laplacian pyramid to process the luminance image subjected to tone mapping, and have complementarity by combining the three mapping methods, so that each region of the HDR image can be fully compressed, and image details can be supplemented.
After the image processing method provided by the invention is used for processing, the dynamic range of the HDR image can be obviously compressed, the image color is obvious, the overall effect is good, the details of the image are clear and visible, the mapping effect meets the visual requirement of a user, and the image can be directly displayed on the traditional equipment. The image processed by the scheme can also be widely applied to medical images, criminal investigation image processing, satellite remote sensing, game special effects and other life scenes.
The following describes the image processing apparatus provided by the present invention, and the image processing apparatus described below and the image processing method described above may be referred to in correspondence with each other.
Fig. 3 is a schematic structural diagram of an image processing apparatus provided in the present invention, and as shown in fig. 3, the apparatus may include:
a first determining module 310, configured to determine a weighted image according to the luminance image after the dynamic range is compressed, and perform gaussian pyramid decomposition on the weighted image;
a decomposition module 320, configured to perform laplacian pyramid decomposition on the luminance image;
a second determining module 330, configured to determine a target image pyramid according to the image subjected to the gaussian pyramid decomposition and the image subjected to the laplacian pyramid decomposition;
and a third determining module 340, configured to determine a luminance output image according to the target image pyramid, and restore the color of the luminance output image.
The image processing device provided by the invention can obviously compress the dynamic range of the HDR image through decomposition and fusion of the double pyramids, simultaneously keeps the contrast and color information of the image, has better overall effect, can well reproduce the image details, effectively solves the halo phenomenon which is difficult to solve by local tone mapping, can display the processed image on the traditional equipment, and meets the daily visual requirements of users.
In one embodiment, before determining a weighted image according to the luminance image after compressing the dynamic range and performing gaussian pyramid decomposition on the weighted image, the method includes:
separating out the brightness component of the high dynamic range image;
determining a luminance image of the high dynamic range image from the luminance component.
In one embodiment, the compressed dynamic range luminance image is determined by:
and compressing the brightness image of the high dynamic range image according to a global tone mapping algorithm and a gamma correction algorithm respectively, and determining the brightness image with the compressed dynamic range.
In one embodiment, the first determining module 310 is specifically configured to:
and determining a weight image according to the contrast, the exposure goodness and the gradient value of the brightness image after the dynamic range is compressed.
In one embodiment, the first determining module 310 is specifically configured to:
and carrying out normalization processing on the weight image, and carrying out Gaussian pyramid decomposition on the weight image after the normalization processing.
In one embodiment, the second determining module 330 is specifically configured to:
and multiplying the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition according to pixel distribution to obtain a target image pyramid.
In an embodiment, the third determining module 340 is specifically configured to:
carrying out Laplacian pyramid reconstruction on the target image pyramid;
and determining a brightness output image according to the reconstructed Laplacian pyramid.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the image processing method provided by the methods when executing the computer program.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a Communication Interface (Communication Interface) 420, a memory (memory) 430 and a Communication bus 440, wherein the processor 410, the Communication Interface 420 and the memory 430 are communicated with each other via the Communication bus 440. The processor 410 may call the computer program in the memory 430 to execute the steps of the image processing method provided by the above embodiments, for example, including:
determining a weight image according to the brightness image after the dynamic range is compressed, and carrying out Gaussian pyramid decomposition on the weight image;
performing Laplacian pyramid decomposition on the brightness image;
determining a target image pyramid according to the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition;
and determining a brightness output image according to the target image pyramid, and recovering the color of the brightness output image.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several 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 another aspect, the present invention also provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, the computer can execute the steps of the image processing method provided by the above embodiments, for example, the steps include:
determining a weight image according to the brightness image after the dynamic range is compressed, and carrying out Gaussian pyramid decomposition on the weight image;
performing Laplacian pyramid decomposition on the brightness image;
determining a target image pyramid according to the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition;
and determining a brightness output image according to the target image pyramid, and restoring the color of the brightness output image.
On the other hand, embodiments of the present application further provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is configured to enable a processor to execute the steps of the image processing method provided in the foregoing embodiments, for example, the steps include:
determining a weight image according to the brightness image after the dynamic range is compressed, and carrying out Gaussian pyramid decomposition on the weight image;
performing Laplacian pyramid decomposition on the brightness image;
determining a target image pyramid according to the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition;
and determining a brightness output image according to the target image pyramid, and recovering the color of the brightness output image.
The processor-readable storage medium may be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid State Disks (SSDs)), etc.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An image processing method, comprising:
determining a weight image according to the brightness image with the compressed dynamic range, and carrying out Gaussian pyramid decomposition on the weight image;
performing Laplacian pyramid decomposition on the brightness image;
determining a target image pyramid according to the image subjected to the decomposition of the Gaussian pyramid and the image subjected to the decomposition of the Laplacian pyramid;
and determining a brightness output image according to the target image pyramid, and restoring the color of the brightness output image.
2. The image processing method according to claim 1, wherein before determining a weighted image from the luminance image after compressing the dynamic range and performing gaussian pyramid decomposition on the weighted image, the method comprises:
separating out the brightness component of the high dynamic range image;
determining a luminance image of the high dynamic range image from the luminance component.
3. The image processing method according to claim 2, wherein the luminance image after the dynamic range compression is determined by:
and compressing the brightness image of the high dynamic range image according to a global tone mapping algorithm and a gamma correction algorithm respectively, and determining the brightness image with the compressed dynamic range.
4. The image processing method according to claim 3, wherein determining the weighted image from the luminance image after compressing the dynamic range comprises:
and determining the weight image according to the contrast, the exposure goodness and the gradient value of the brightness image after the dynamic range is compressed.
5. The image processing method of claim 4, wherein the performing Gaussian pyramid decomposition on the weighted image comprises:
and carrying out normalization processing on the weight image, and carrying out Gaussian pyramid decomposition on the weight image after the normalization processing.
6. The method of claim 5, wherein determining a target image pyramid from the Gaussian pyramid decomposed image and the Laplacian pyramid decomposed image comprises:
and multiplying the image subjected to the Gaussian pyramid decomposition and the image subjected to the Laplacian pyramid decomposition according to pixel distribution to obtain a target image pyramid.
7. The method of claim 6, wherein determining a luminance output image according to the target image pyramid comprises:
performing Laplacian pyramid reconstruction on the target image pyramid;
and determining the brightness output image according to the reconstructed Laplacian pyramid.
8. An image processing apparatus characterized by comprising:
the first determining module is used for determining a weight image according to the brightness image after the dynamic range is compressed and carrying out Gaussian pyramid decomposition on the weight image;
the decomposition module is used for carrying out Laplacian pyramid decomposition on the brightness image;
the second determining module is used for determining a target image pyramid according to the image subjected to the decomposition of the Gaussian pyramid and the image subjected to the decomposition of the Laplacian pyramid;
and the third determining module is used for determining a brightness output image according to the target image pyramid and recovering the color of the brightness output image.
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 the processor realizes the steps of the image processing method according to any one of claims 1 to 7 when executing the computer program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the image processing method according to any one of claims 1 to 7.
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