CN110246086B - Image processing method for color image enhancement and image processing system thereof - Google Patents
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Abstract
The invention discloses an image processing method, which comprises the following steps: acquiring a color image to be processed and extracting brightness channel information of the color image to be processed, wherein the brightness information image is set as a reference image; decomposing the reference image to obtain a luminance base layer and a luminance detail layer; respectively carrying out local mapping on the image base layer and the brightness detail layer to obtain a brightness mapping image; and reconstructing RGB channel information of the color image to be processed based on the luminance mapping image and the reference image.
Description
Technical Field
The present invention relates to the field of image processing, and more particularly, to an image processing method for color image enhancement and image processing thereof
Background
With the development of technology, computing devices are increasingly being used in various levels of modern society and make great contributions to the development of modern society, including, but not limited to, digital cameras, video cameras, smart phones, navigation systems, and the like. In particular, in recent years, apparatuses having a function of capturing images, such as digital cameras, have become increasingly popular, and imaging quality thereof is increasingly demanded.
Fig. 1 is a schematic diagram of a conventional image capturing apparatus, which includes an optical lens 1P, a photosensitive chip 2P, an Analog-to-digital converter (AD) 3P and an image processor 4P. Particularly, when the photosensitive chip 2P is implemented as a photosensitive chip having a function of collecting color images, the photosensitive chip 2P further includes a color filter 5P to obtain a color image by the color filter 5P and a subsequent image processing algorithm.
The range of light brightness in nature is very wide, and the dynamic range can reach 106However, existing image capture devices are typically only capable of capturing images of a limited dynamic luminance range. Therefore, the actually acquired color image often shows overexposure in the highlight area, and shows underexposure in the corresponding low-brightness area, so that the specific image contents of the low-brightness area and the highlight area cannot be identified by naked eyes, and the normal visual effect of human eyes is not met. Therefore, image enhancement processing is required for the color image.
Existing methods commonly used for color image enhancement are tone mapping methods, which include global tone mapping algorithms and local tone mapping methods. The global tone mapping method is that each pixel point on the image is mapped according to the characteristics and the brightness information of the pixel point in the whole image field, and the RGB channel information of the color image is reconstructed according to the brightness change of the pixel point before and after mapping. The method is simple and convenient to calculate and efficient in efficiency, but in the mapping process, the specific spatial position of the pixel point in the full image is not considered, and the mapping in the same mode is carried out only on the basis of the brightness information of the pixel, so that the reconstructed color image has certain losses in the aspects of chroma, lightness, image details and the like.
The local tone mapping method is an improvement of the global tone mapping method, and in the process of image mapping, the spatial position of the pixel point and the brightness information of the pixel point are taken into consideration simultaneously, so that the tone mapping effect is obviously better than that of the global tone mapping method.
However, in practical applications, the existing local tone mapping methods are mostly only suitable for application to high dynamic range images, but not for common RGB images. For example, tone mapping methods based on gradient domain compression cannot achieve satisfactory optimization due to the limitation of the number of bits (bit) of the color image. Meanwhile, most of local tone methods applied to color images are complex in algorithm and high in calculation complexity, and a halo artifact phenomenon is easy to occur after image processing, so that the visual requirements of users on the imaging quality of the color images cannot be met.
Disclosure of Invention
The invention mainly aims to provide an image processing method for color image enhancement and an image processing system thereof, wherein the image processing method enhances a color image based on a local tone mapping method so that the finally reconstructed color image has a high human visual effect.
Another object of the present invention is to provide an image processing method for color image enhancement and an image processing system thereof, wherein the image processing method can enhance a color image while effectively maintaining detailed information of the image, such as edges, contours, textures, and the like.
Another object of the present invention is to provide an image processing method for color image enhancement and an image processing system thereof, wherein the image processing method and the image processing system can be applied to process both high dynamic range images (HDR) and common RGB images, and have relatively superior compatibility and versatility.
Another object of the present invention is to provide an image processing method for enhancing a color image and an image processing system thereof, wherein the image processing method decomposes the color image into a detail layer and a base layer by using a correlation algorithm in the process of enhancing the color image, and performs different tone mappings on the base layer and the detail layer, respectively, in such a way that the detail information of the image can be effectively maintained as much as possible while the visual effect of the color image is improved.
It is another object of the present invention to provide an image processing method of color image enhancement and an image processing system thereof, wherein the image processing method divides a color image into a detail layer and a base layer, and tone maps the base layer and the detail layer, respectively, in such a way that the amount of computation of the image processing algorithm is simplified to reduce the requirements on processor hardware.
Another aspect of the present invention is to provide an image processing method for color image enhancement and an image processing system thereof, wherein the image processing method maps an image base layer in a manner that: the brightness of the bright area in the image is kept unchanged, the brightness of the low bright area of the image and the brightness of the high bright area of the compressed image are improved, so that the visual effect of the optimized image base layer is more natural, and the image base layer has higher fidelity.
Another object of the present invention is to provide an image processing method for color image enhancement and an image processing system thereof, wherein the image processing method maps image detail layers in a manner that: and stretching the part with darker brightness and smaller image detail to a large extent, and stretching the part with brighter brightness and larger image detail to a small extent, so that the reconstructed image has relatively better local contrast.
Another objective of the present invention is to provide an image processing method for color image enhancement and an image processing system thereof, wherein the image processing system can be integrated into any image capturing device or any electronic device with an image capturing function, so as to optimize a color image captured by the image capturing device or the electronic device, thereby improving a visual effect of the image.
Other advantages and features of the invention will become apparent from the following description and may be realized by means of the instrumentalities and combinations particularly pointed out in the appended claims.
In accordance with the present invention, the foregoing and other objects and advantages are achieved by an image processing method for color image enhancement, comprising the steps of:
s1, acquiring a color image to be processed and extracting the brightness channel information of the color image to be processed, wherein the brightness information image is set as a reference image;
s2 decomposing the reference image to obtain a luminance base layer and a luminance detail layer;
s3 performing local mapping on the image base layer and the luminance detail layer respectively to obtain a luminance mapped image; and
s4 reconstructs RGB channel information of the color image to be processed based on the luminance mapped image and the reference image.
In an embodiment of the present invention, the step S1 further includes the steps of:
s11, acquiring a color image to be processed in an RGB format;
s12, converting the color image to be processed in RGB format into a color image to be processed in YUV format; and
s13 extracts the Y component of the YUV format color image to be processed to obtain the reference image.
In an embodiment of the present invention, between the step S2 and the step S3, the method further includes the steps of:
s20 mapping the S-shaped curve of the brightness base layer, wherein B*＝Scurve(B)，
Wherein B is*Denotes the luminance base layer after S-shaped curve mapping, B denotes the luminance base layer and Scurve(x) Is a functional mapping form of the sigmoid curve.
In an embodiment of the present invention, the step S3 further includes the steps of:
s30 locally maps the luminance base layer and the luminance detail layer after the S-shaped curve mapping, respectively, to obtain the luminance mapped image.
In an embodiment of the present invention, the step S2 further includes the steps of:
s21 filtering the reference image by a filter based on image detail information to obtain the luminance base layer; and
s22 subtracts the reference image from the luminance base layer to obtain the luminance detail layer D, where D is L-B.
In an embodiment of the present invention, the step S2 further includes the steps of:
S21A processing the reference image by an algorithm for extracting image detail information to obtain the luminance detail layer; and
S22A performs subtraction on the reference image and the luminance detail layer to obtain the luminance base layer.
In an embodiment of the present invention, the step S21 further includes the steps of:
s211, carrying out multi-scale filtering processing on the reference image to acquire a series of filtered image information with different scales; and
s212 synthesizing the filtered image information with different scales to obtain the luminance base layer;
in an embodiment of the present invention, in the step S21, the filter for performing the filtering process based on the image detail information on the reference image may be selected from any one of a group consisting of a bilateral filter, a bootstrap filter and a local edge preserving filter
In an embodiment of the present invention, the step S3 further includes the steps of:
s31 local tone mapping the luminance base layer, wherein the mapping Curve of the luminance base layer is set as CurveBAnd the luminance base layer Scale curve is ScaleBWherein CurveBIs a cubic spline interpolation function with interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments are polynomial interpolation, the middle segment is linear interpolation, the interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are belonged to [0,1 ]],b≤c,d∈[-1,0]；
S32, local adjustment mapping is performed on the brightness detail layer, wherein the mapping Curve of the brightness detail layer is set as CurveDAnd the brightness detail layer Scale curve is ScaleDWherein CurveDIs a cubic spline interpolation function with interpolation points-1,0),(-a,-b),(0,0),(a,b),(1,0)，a,b∈[0,1](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]](ii) a And
s33 obtaining the luminance map image, wherein the luminance map image is formulated as: l isltm＝wBCurveB(L)ScaleB(B)+wDCurveD(D)ScaleD(B) + L, wherein the LltmRepresenting the luminance map image; w is aB、wDIs the corresponding weighting factor, L is the reference image, B is the luminance base layer and D is the luminance detail layer.
In an embodiment of the present invention, the step S30 further includes the steps of:
S31A local tone mapping the luminance base layer after S-shaped Curve mapping, wherein the mapping Curve of the luminance base layer is set as CurveBAnd the luminance base layer Scale curve is ScaleBWherein CurveBIs a cubic spline interpolation function with interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments are polynomial interpolation, the middle segment is linear interpolation, the interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are belonged to [0,1 ]],b≤c,d∈[-1,0]；
S32A local tone mapping the luminance detail layer, wherein the mapping Curve of the luminance detail layer is set as CurveDAnd the brightness detail layer Scale curve is ScaleDWherein CurveDIs a cubic spline interpolation function with interpolation points (-1,0), (-a, -b), (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]](ii) a And
S33A, acquiring the brightness map image, wherein the brightness map image is formulated as: l isltm＝wBCurveB(L)ScaleB(B*)+wDCurveD(D)ScaleD(B*) + L, wherein the LltmRepresenting the luminance map image; w is aB、wDIs a corresponding weight factor, L is the reference image，B*For the luminance base layer after sigmoid curve mapping and D for the luminance detail layer.
In an embodiment of the present invention, the step S4 further includes the steps of:
s41 is a process for solving a local gain according to the luminance change before and after the reference image mapping, wherein the local gain is expressed by the following equation:
wherein L isltmRepresenting the luminance map image, L representing the reference image; and
s42, reconstructing the RGB channel information of the color image to be processed according to the local gain, wherein the reconstruction relation is expressed by formula:
s5 outputs the color image after reconstruction.
According to another aspect of the present invention, there is also provided an image processing system comprising:
an acquisition module;
a decomposition module;
a processing module; and
the acquisition module, the decomposition module, the processing module and the reconstruction module are mutually connected in a communication way, and the acquisition module is used for acquiring a color image to be processed and extracting the brightness channel information of the color image to be processed; the decomposition module is connected with the acquisition module in a communication way and is used for decomposing the reference image to obtain a brightness base layer and a brightness detail layer; the processing module is communicably connected to the decomposition module and is used for respectively performing local mapping on the image base layer and the brightness detail layer to obtain a brightness mapping image; the reconstruction module is communicably connected to the acquisition module and the processing module, and is configured to reconstruct the RGB channel information of the color image to be processed based on the luminance mapping image and the reference image.
Further objects and advantages of the invention will be fully apparent from the ensuing description and drawings.
These and other objects, features and advantages of the present invention will become more fully apparent from the following detailed description, the accompanying drawings and the claims.
Drawings
Fig. 1 is a schematic diagram of a conventional image capturing apparatus.
FIG. 2 is a block diagram of an image processing system according to a preferred embodiment of the present invention.
Fig. 3 is a flowchart illustrating an image processing method according to the above preferred embodiment of the present invention.
Fig. 4 is a schematic block diagram illustrating a step of acquiring a color image to be processed according to the image processing method.
Fig. 5 is a block diagram illustrating a step of decomposing the reference image to obtain a luminance base layer and a luminance detail layer according to the above-mentioned image processing method.
Fig. 6 is a block diagram illustrating a step of filtering the reference image by a filter based on image detail information to obtain the luminance base layer according to the image processing method. Fig. 7 is a block diagram illustrating the step of mapping the image base layer and the luminance detail layer locally according to the above-mentioned image processing method.
Fig. 8 is a block diagram schematically illustrating the step of reconstructing the color information of the color image to be processed according to the above-mentioned image processing method.
Fig. 9 is a flowchart illustrating another image processing method according to the above preferred embodiment of the invention.
Fig. 10 is a block diagram illustrating the partial mapping of the luminance base layer and the luminance detail layer after the S-shaped curve mapping according to another image processing method described above.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The basic principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that in the present disclosure, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship indicated in the drawings for ease of description and simplicity of description, and do not indicate or imply that the referenced devices or components must be constructed and operated in a particular orientation and thus are not to be considered limiting.
It is to be understood that the terms "a" and "an" are to be interpreted as meaning that a number of elements in one embodiment may be one and a number of elements in another embodiment may be plural, and the terms "a" and "an" are not to be interpreted as limiting the number.
As shown in fig. 2, a color image enhanced image processing system according to a preferred embodiment of the present invention is illustrated, wherein the image processing system can be integrated into any image capturing device, such as a digital camera, a video camera, etc., or any electronic device with an image capturing function, such as a smart phone, a tablet computer, etc., for performing effect enhancement on the color image captured by the image capturing device or the electronic device, so as to improve the final visual effect of the image and facilitate the subsequent research and development of related applications based on image information. Generally, the image processing system may be integrated into a processor of the image capture device or a processor of the electronic device and performs image enhancement on the acquired color image according to a preloaded image processing program.
As mentioned above, however, the existing image capturing devices or electronic devices can capture only images with limited dynamic range, so that the actually captured color images often show overexposure in bright areas and underexposure in dark areas, so that the specific image content in dark areas cannot be recognized by naked eyes, and neither of them can meet the normal visual effect of human eyes. Therefore, image enhancement is required for color images.
It should be noted that, corresponding to the morphological feature of the detected object and the light and shade condition of the shooting environment, the unused area of the image has different features, therefore, when the image is processed correspondingly to enhance the final imaging effect, the different features of the different areas of the image in the actual image acquisition process should be fully considered, and the image is processed with pertinence by using the features reasonably, so that the final image imaging effect can meet the visual requirement of the human eyes.
More specifically, the present invention provides the image correction system, which operates according to the image processing method of color image enhancement as described below based on the basic idea of local tone mapping. As shown in fig. 3, the image processing method includes the steps of:
s1, acquiring a color image to be processed and extracting the brightness channel information of the color image to be processed, wherein the brightness information image is set as a reference image;
s2 decomposing the reference image to obtain a luminance base layer and a luminance detail layer;
s3 performing local mapping on the image base layer and the luminance detail layer respectively to obtain a luminance mapped image; and
s4 reconstructs RGB channel information of the color image to be processed based on the luminance mapped image and the reference image.
Particularly, in the invention, in consideration of high correlation among RGB three information channels, in order to reduce unnecessary color cast phenomenon introduced in the process of local tone mapping as much as possible, tone mapping is selected to be applied to a brightness channel, and then local gain change before and after local mapping is utilized to correspondingly adjust the RGB three information channels. This is the design starting point of step S1.
Those skilled in the art will appreciate that the RGB color image information represents the chrominance, luminance, saturation and other information in a mixed manner, and therefore, in the process of executing step S1, the RGB color image information needs to be converted into YUV color image information to separately extract the luminance channel information of the color image, where Y represents the luminance information of the image, and U and V represent the chrominance information of the image, respectively.
In particular, in the preferred implementation of the present invention, the method for extracting luminance channel information of the color image to be processed with RGB format can be expressed by the following formula: l ═ wRR+wGG+wBB+wmaxmax(R,G,B)+wminmin (R, G, B), wherein wR+wG+wB+wmax+w min1 is ═ 1; l denotes an image formed by the luminance information Y, i.e., the reference image. Those skilled in the art will appreciate that, in general, defaults are: w is aR＝0.299,wG＝0.587,wB＝0.114,wmin＝0,wmaxHowever, in the present invention, the parameter W may be set according to the actual situation of the captured imageR、WG、WB、WminAnd WmaxAnd making a slight adjustment to make the extracted luminance channel information more suitable for the subsequent processing of the image processing method provided by the invention. For example, in one embodiment of the present invention, the parameter WR、WG、WB、WminAnd WmaxCan be set as follows: w is aR＝0.125,wG＝0.25,wB＝0.125,wmin＝0,wmax＝0.5。
Accordingly, in the preferred embodiment of the present invention, as shown in fig. 4, the step S1 further includes the steps of:
s11, acquiring a color image to be processed in RGB format;
s12, converting the color image to be processed in RGB format into a color image to be processed in YUV format; and
s13 extracts the Y component of the color YUV to-be-processed image to obtain the reference image.
Further, as mentioned above, the unused areas of the image have different characteristics corresponding to the morphological characteristics of the target to be detected and the brightness conditions of the shooting environment, and when the image is processed correspondingly, the different characteristics of the different areas of the image in the actual image acquisition process should be fully considered, and the characteristics are reasonably utilized to process the image in a targeted manner, so that the final image imaging effect meets the normal visual requirements of human eyes. Accordingly, in the preferred embodiment of the present invention, the reference image (luminance information image) is selectively decomposed into a luminance base layer image and a luminance detail layer image, wherein the luminance base layer image includes most of the image information (flat region) of the target to be detected, and the luminance detail layer image includes detail information of the target to be detected, such as edges, contours, or textures, in such a way as to allow the luminance base layer and the luminance detail layer to be individually adaptively processed in the subsequent tone mapping process, thereby ensuring that the detail information of the image, such as edges, contours, and textures, etc., can be effectively maintained while the color image is enhanced. Accordingly, this is the design initiative of step S2.
More specifically, in the preferred embodiment of the present invention, the reference image may be processed with a filter based on image detail information to obtain the luminance base layer and the luminance detail layer. For example, in a specific embodiment of the present invention, the image processing method uses a bilateral filter based on image detail information to process the reference image to obtain the luminance base layer, where the bilateral filter is expressed by the following formula:
Further, after the luminance base layer B is obtained by the bilateral filter, the reference image L and the luminance base layer B may be subtracted to obtain the luminance detail layer D, where D is L-B. That is, in the present invention, the reference information is composed of the image base layer and the image detail layer.
It should be noted that, in the present invention, the filter based on the image detail information may be selected from other types, such as: a Local Edge-preserving (Local Edge-preserving) filter, or a Guided Image Filtering (GIF), etc. Those skilled in the art will readily understand that, in the present invention, the type of the filter based on the image detail information is not limited by the present invention, and it is only necessary to be able to extract the luminance base layer. Accordingly, in the preferred embodiment of the present invention, as shown in fig. 5, the step S2 further includes the steps of:
s21 filtering the reference image by a filter based on image detail information to obtain the luminance base layer; and
s22 subtracts the reference image from the luminance base layer to obtain the luminance detail layer D, where D is L-B.
Accordingly, in the step S21, the filter for performing the filtering process based on the image detail information on the reference image may be selected from any one of the group consisting of a bilateral filter, a bootstrap filter, and a local edge-preserving filter.
Furthermore, those skilled in the art will appreciate that in most cases, detailed information of an image, such as edges, contours, or textures, is represented on a different scale. Therefore, in order to make up for the deficiency that all detail features of a sharp image cannot be obtained in a single scale, in the invention, multi-scale filtering processing can be selected to be performed on the reference image.
More specifically, in the preferred embodiment of the present invention, the reference image may be subjected to multi-scale filtering processing to obtain a series of filtered image information with different scales, and further, the filtered image information with different scales may be synthesized to obtain the luminance base layer. It should be easily understood that, in the process of performing multi-scale decomposition on the reference image, all the detail features of the reference image are decomposed into images with different scales one by one, so that the defect that the detail features of the images are omitted in a single-scale processed image can be effectively overcome.
Accordingly, in the present invention, as shown in fig. 6, the step S21 further includes the steps of:
s211, carrying out multi-scale filtering processing on the reference image to acquire a series of filtered image information with different scales; and
s212 synthesizing the filtered image information with different scales to obtain the luminance base layer;
for ease of calculation and to meet certain accuracy requirements, the dimension of the decomposition is typically set to 3. That is, after step S211 is performed, the exploded image information with 3 different scales can be obtained, and after step S212 is performed, the filtered image information with 3 different scales can be obtained, which are respectively expressed as: low-scale filtered image information B1Intermediate-scale filtering image information B2And high-scale filtering image information B3. In particular, in step S213, the selection may be made according to B ═ B (B)1+B2+B3) And/3, obtaining the brightness base layer by averaging the filtered image information of different scales. It is worth mentioning that in other embodiments of the present invention, the decomposition scale may be set to other values, wherein as the decomposition scale increases, the detail information of the reference image is extracted relatively more completely, but the amount of calculation increases; when the decomposition scale is reduced, the amount of calculation is reduced, but the details of the reference image are reducedThe integrity with which information can be extracted is reduced.
It will be readily appreciated by those skilled in the art that in alternative embodiments of the present invention, the image detail layer may be preferentially extracted using a correlation algorithm and further differenced using the reference image and the image detail layer to obtain the image base layer. That is, in another embodiment of the present invention, the image detail layer may be acquired first, and then the image base layer may be acquired.
In an embodiment of the invention, the image processing method may process the reference image by an algorithm for extracting image detail information to obtain the luminance detail layer, and further, perform a subtraction on the reference image and the luminance detail layer to obtain the luminance detail layer. That is, in another embodiment of the present invention, as shown in fig. 5, the step S2 further includes the steps of:
S21A processing the reference image by an algorithm for extracting image detail information to obtain the luminance detail layer; and
S22A performs subtraction on the reference image and the luminance detail layer to obtain the luminance base layer.
Further, after the luminance detail layer and the luminance basic layer are obtained by the correlation algorithm, a suitable local tone mapping algorithm needs to be designed for the image characteristics corresponding to the luminance detail layer and the luminance basic layer, so that the final color image enhancement effect meets the preset requirement. As mentioned above, the actual color image effect often appears dark in the low-brightness area of the image, so that the specific imaging content cannot be recognized by naked eyes, and correspondingly, the brightness in the high-brightness area of the image is abnormally high, which is not suitable for the normal effect of human eyes. For these features, when mapping the luminance base layer, the selectable strategies are: and keeping the brightness of the middle bright area unchanged, improving the brightness of the low bright area, and compressing the brightness of the high bright area, so that the image subjected to local mapping has higher naturalness and structural fidelity. Meanwhile, when mapping the luminance detail layer, the selectable strategies are: and stretching the part with darker brightness and smaller image detail to a large extent, and stretching the part with brighter brightness and larger image detail to a small extent so as to enhance the local contrast of the image after local mapping.
More specifically, in the preferred embodiment of the present invention, as shown in fig. 7, the step S3 further includes the steps of:
s31 local tone mapping the luminance base layer, wherein the mapping Curve of the luminance base layer is set as CurveBAnd the luminance base layer Scale curve is ScaleBWherein CurveBIs a cubic spline interpolation function with interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments are polynomial interpolation, the middle segment is linear interpolation, the interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are belonged to [0,1 ]],b≤c,d∈[-1,0]；
S32 local tone mapping the luminance detail layer, wherein the mapping Curve of the luminance detail layer is set as CurveDAnd the brightness detail layer Scale curve is ScaleDWherein CurveDIs a cubic spline interpolation function with interpolation points (-1,0), (-a, -b), (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]](ii) a And
s33 obtaining the luminance map image, wherein the luminance map image is formulated as: l isltm＝wBCurveB(L)ScaleB(B)+wDCurveD(D)ScaleD(B) + L, wherein the LltmRepresenting the luminance map image; w is aB、wDIs the corresponding weighting factor, L is the reference image, B is the luminance base layer and D is the luminance detail layer.
It should be noted that, in step S31, the CurveBIs a cubic spline interpolation function, the interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments are polynomial interpolation, the middle segment is linear interpolation, the interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are belonged to [0,1 ]],b≤c,d∈[-1,0]. Accordingly, in step S33, the function Curve is utilizedB(L)*ScaleB(B) Can correspond toAnd adjusting the middle bright area, the low bright area and the high bright area of the brightness base layer in different modes, namely keeping the brightness of the middle bright area unchanged, and improving the brightness of the low bright area and compressing the brightness of the high area.
It will be appreciated that this corresponds to ScaleBLeft segment function of the Curve (coordinate (0, a) - - (b,0) segment function), function CurveB(L)*ScaleB(B) The corresponding function value of (b) is suitably increased at this stage, from the viewpoint of the actual image effect, i.e. the brightness of the low-brightness region of the brightness base layer is increased. Corresponding to ScaleBMiddle function of the Curve (coordinate (b,0) - - (c,0) segment function), function CurveB(L)*ScaleB(B) The corresponding function value of (b) is maintained constant at this stage, i.e. the brightness of the bright area in the brightness base layer is maintained from the actual image effect. Corresponding to ScaleBRight segment function of the Curve (coordinate (c,0) - - (1, d) segment function), function CurveB(L)*ScaleB(B) The corresponding function value of (b) is appropriately decreased, i.e., the luminance value of the highlight region of the luminance base layer is kept compressed from the viewpoint of the actual image effect.
Similarly, in step S2, the Curve CurveDIs a cubic spline interpolation function with interpolation points (-1,0), (-a, -b), (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]]. Accordingly, in step S33, the function Curve is utilizedD(D)ScaleD(B) The small-detail and large-detail regions corresponding to the luminance detail layer can be adjusted in different ways, namely: and stretching the part with darker brightness and smaller image detail to a large extent, and stretching the part with brighter brightness and larger image detail to a small extent, so that the reconstructed image has relatively better local contrast.
It will be appreciated that this corresponds to the curve ScaleDThe first segment function (coordinate (0, a) - - (b, c) segment function) of (1), the function CurveD*ScaleD(B) The corresponding function value of (2) is greatly increased at this stage, and from the view point of the actual image effect, the image with dark brightness and small details is greatly stretched. Further, it corresponds to the curve ScaleDSecond section ofNumber (coordinates (b, c) - - (1, d) segment function), function CurveD*ScaleD(B) The corresponding function value of (a) increases at this stage by a small margin, i.e. stretching by a small margin where the brightness is brighter and the image has greater detail, from the actual image effect.
It is noted that, in order to optimize the local mapping effect of the step S3(S31, S32, and S33), the luminance base layer may be enhanced using a correlation algorithm before the step S3(S31, S32, and S33) is performed. More specifically, in the preferred embodiment of the present invention, between steps S2 and S3, as shown in fig. 9, the method further comprises the steps of:
s20 mapping the S-shaped curve of the brightness base layer, wherein B*＝Scurve(B)，
Wherein B is*Denotes the luminance base layer after S-shaped curve mapping, B denotes the luminance base layer and Scurve(x) Is a functional mapping form of the sigmoid curve.
It will be appreciated that in step S20, the luminance base layer is mapped with an S-shaped curve to appropriately enhance the luminance base layer, in such a way as to facilitate the implementation of the local mapping algorithm for the luminance base layer in the subsequent step S3.
Accordingly, the subsequent step S3 is specifically implemented as:
s30 locally maps the luminance base layer and the luminance detail layer after the S-shaped curve mapping, respectively, to obtain the luminance mapped image.
Further, as shown in fig. 10, the step 30 further includes the steps of:
S31A local tone mapping the luminance base layer after S-shaped Curve mapping, wherein the mapping Curve of the luminance base layer is set as CurveBAnd the luminance base layer Scale curve is ScaleBWherein CurveBIs a cubic spline interpolation function with interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments of which are polynomialsInterpolation, the middle segment is a linear interpolation whose interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are [0,1 ]],b≤c,d∈[-1,0]；
S32A performing layout tone mapping on the brightness detail layer, wherein the mapping Curve of the brightness detail layer is set as CurveDAnd the brightness detail layer Scale curve is ScaleDWherein CurveDIs a cubic spline interpolation function with interpolation points (-1,0), (-a, -b), (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]](ii) a And
S33A, acquiring the brightness map image, wherein the brightness map image is formulated as: l isltm＝wBCurveB(L)ScaleB(B*)+wDCurveD(D)ScaleD(B*) + L, wherein the LltmRepresenting the luminance map image; w is aB、wDIs a corresponding weight factor, L is the reference image, B*For the luminance base layer after sigmoid curve mapping and D for the luminance detail layer.
Further, the step S3 or S30 is performed to obtain the brightness mapping image LltmThen, the change of the brightness reference image before and after local mapping is needed to be used to correspond to the color information change of the image to be processed, so as to finally realize the effect of color image enhancement.
More specifically, in the preferred embodiment of the present invention, as shown in fig. 8, the step S4 further includes the steps of:
s41 is a process for solving a local gain according to the luminance change before and after the reference image mapping, wherein the local gain is expressed by the following equation:
wherein L isltmRepresenting the luminance map image, L representing the reference image; and
s42, reconstructing the RGB channel information of the color image to be processed according to the local gain, wherein the reconstruction relation is expressed by formula:
wherein
Max is the number of gray levels of the reference image, and w is the weight factor of the corresponding term.
It should be appreciated that in steps S41 and S42, the RGB channel information of the color image to be processed is reconstructed by using the luminance change of the luminance reference image before and after mapping, so that the color cast phenomenon can be maximally avoided. As mentioned above, there is a high correlation between the three RGB information channels of the color image to be processed, so that tone mapping is selectively applied to the luminance channel, and the three RGB information channels are correspondingly adjusted by using the local gain variation of the luminance channel information before and after local mapping. That is, in the invention, the correlation between Y and RGB three-channel information is utilized, and RGB three channels are treated as an integral quantity to be adjusted, so that unnecessary color cast introduced in the process of local tone mapping can be reduced as much as possible.
Accordingly, after performing color image enhancement, the color image after the reconstruction may be selected to be output. That is, in the preferred embodiment of the present invention, the image processing method further includes the steps of:
s5 outputs the color image after reconstruction.
Further, as shown in fig. 2, the image processing system provided by the present invention includes an obtaining module, a decomposition module, a processing module and a reconstruction module, wherein the obtaining module, the decomposition module, the processing module and the reconstruction module are communicably connected to each other to perform color image enhancement processing on the obtained image to be processed according to a preset image processing method.
More specifically, the acquiring module is used for acquiring a color image to be processed and extracting the luminance channel information of the color image to be processed, wherein the luminance information image is taken as a reference image. The decomposition module is communicatively connected to the acquisition module for decomposing the reference image to obtain a luminance base layer and a luminance detail layer. The processing module is communicably connected to the decomposition module for performing local mapping on the image base layer and the luminance detail layer, respectively, to obtain a luminance mapping image. The reconstruction module is communicably connected to the acquisition module and the processing module, and is configured to reconstruct the RGB channel information of the color image to be processed based on the luminance mapping image and the reference image.
Further, in an embodiment of the present invention, the image processing system further includes an image output module, wherein the image output module is communicably connected to the reconstruction module for outputting the color image after reconstruction.
It can thus be seen that the objects of the invention are sufficiently well-attained. The embodiments illustrated to explain the functional and structural principles of the present invention have been fully illustrated and described, and the present invention is not to be limited by changes based on the principles of these embodiments. Accordingly, this invention includes all modifications encompassed within the scope and spirit of the following claims.
Claims (18)
1. A method of image processing for color image enhancement, comprising the steps of:
acquiring a color image to be processed and extracting brightness channel information of the color image to be processed, wherein a brightness information image is set as a reference image;
decomposing the reference image to obtain a luminance base layer and a luminance detail layer;
respectively carrying out local mapping on the brightness base layer and the brightness detail layer to obtain a brightness mapping image; and
reconstructing RGB channel information of the color image to be processed based on the brightness mapping image and the reference image;
wherein the step of separately mapping the luminance base layer and the luminance detail layer to obtain a luminance mapped image further comprises the steps of:
performing local tone mapping on the luminance base layer, wherein the mapping Curve of the luminance base layer is set as CurveBAnd the luminance base layer Scale curve is ScaleBWherein CurveBIs a cubic spline interpolation function with interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments are polynomial interpolation, the middle segment is linear interpolation, the interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are belonged to [0,1 ]],b≤c,d∈[-1,0]；
Performing local tone mapping on the luminance detail layer, wherein the mapping Curve of the luminance detail layer is set as CurveDAnd the brightness detail layer Scale curve is ScaleDWherein CurveDIs a cubic spline interpolation function with interpolation points (-1,0), (-a, -b), (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]](ii) a And
obtaining the brightness mapping image, wherein the brightness mapping image is formulated as: l isltm＝wBCurveB(L)ScaleB(B)+wDCurveD(D)ScaleD(B) + L, wherein the LltmRepresenting the luminance map image; w is aB、wDIs the corresponding weighting factor, L is the reference image, B is the luminance base layer and D is the luminance detail layer.
2. The image processing method of claim 1, wherein the step of decomposing the reference image to obtain a luminance base layer and a luminance detail layer further comprises the steps of:
filtering the reference image by a filter based on image detail information to obtain the brightness base layer;
and subtracting the reference image from the luminance base layer to obtain the luminance detail layer.
3. The image processing method as claimed in claim 2, wherein the step of filtering the reference image by a filter based on image detail information to obtain the luminance base layer further comprises the steps of:
carrying out multi-scale filtering processing on the reference image to obtain a series of filtered image information with different scales; and
filtered image information having different scales is synthesized to obtain the luminance base layer.
4. The image processing method as claimed in claim 2, wherein in the step of filtering the reference image by an image detail information-based filter to obtain the luminance base layer, the filter for filtering the reference image based on the image detail information is selected from any one of the group consisting of a bilateral filter, a guided filter and a local edge preserving filter.
5. The image processing method of claim 1, wherein the step of decomposing the reference image to obtain a luminance base layer and a luminance detail layer further comprises the steps of:
processing the reference image by an algorithm for extracting image detail information to obtain the brightness detail layer; and
the reference image is subtracted from the luminance detail layer to obtain the luminance base layer.
6. The image processing method according to any one of claims 1 to 5, wherein said step of reconstructing color information of the color image to be processed based on the luminance mapped image and the reference image further comprises the steps of:
solving a local gain according to the brightness change before and after the reference image mapping, wherein the local gain is expressed by a formula:
wherein L isltmRepresenting the luminance map image, L representing the reference image; and
reconstructing RGB channel information of the color image to be processed according to the local gain, wherein the reconstruction relation is expressed by a formula as follows:
7. The image processing method according to claim 1, wherein said step of obtaining a color image to be processed and extracting luminance channel information of the color image to be processed, wherein the luminance information image is set as a reference image, further comprises the steps of:
acquiring a color image to be processed in an RGB format;
converting the color image to be processed in the RGB format into a color image to be processed in a YUV format; and
and extracting a Y component of the color image to be processed in the YUV format to obtain the reference image.
8. The image processing method according to claim 1, wherein the image processing method further comprises the steps of:
and outputting the reconstructed color image.
9. The image processing method according to claim 7, wherein the image processing method further comprises the steps of:
and outputting the reconstructed color image.
10. A method of image processing for color image enhancement, comprising the steps of:
acquiring a color image to be processed and extracting brightness channel information of the color image to be processed, wherein a brightness information image is set as a reference image;
decomposing the reference image to obtain a luminance base layer and a luminance detail layer;
respectively carrying out local mapping on the brightness base layer and the brightness detail layer to obtain a brightness mapping image; and
reconstructing RGB channel information of the color image to be processed based on the brightness mapping image and the reference image;
wherein between the step of decomposing the reference image to obtain a luminance base layer and a luminance detail layer and the step of locally mapping the luminance base layer and the luminance detail layer, respectively, to obtain a luminance mapped image, the method further comprises the steps of:
mapping S-shaped curve of the luminance base layer, wherein B*＝Scurve(B)，
Wherein B is*Denotes the luminance base layer after S-shaped curve mapping, B denotes the luminance base layer and Scurve(x) Is a function mapping form of the S-shaped curve;
wherein the step of separately mapping the luminance base layer and the luminance detail layer to obtain a luminance mapped image further comprises the steps of:
performing local tone mapping on the luminance base layer after S-shaped Curve mapping, wherein the mapping Curve of the luminance base layer is set as CurveBAnd the luminance base layer Scale curve is ScaleBWherein CurveBIs a cubic spline interpolation function with interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments are polynomial interpolation, the middle segment is linear interpolation, the interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are belonged to [0,1 ]],b≤c,d∈[-1,0]；
Performing local tone mapping on the luminance detail layer, wherein the mapping Curve of the luminance detail layer is set as CurveDAnd the brightness is fineThe Scale curve of the node layer is ScaleDWherein CurveDIs a cubic spline interpolation function with interpolation points (-1,0), (-a, -b), (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]](ii) a And
obtaining the brightness mapping image, wherein the brightness mapping image is formulated as: l isltm＝wBCurveB(L)ScaleB(B*)+wDCurveD(D)ScaleD(B*) + L, wherein the LltmRepresenting the luminance map image; w is aB、wDIs a corresponding weight factor, L is the reference image, B*For the luminance base layer after sigmoid curve mapping and D for the luminance detail layer.
11. The image processing method of claim 10, wherein the step of decomposing the reference image to obtain a luminance base layer and a luminance detail layer further comprises the steps of:
filtering the reference image by a filter based on image detail information to obtain the brightness base layer;
and subtracting the reference image from the luminance base layer to obtain the luminance detail layer.
12. The image processing method as claimed in claim 11, wherein the step of filtering the reference image by a filter based on image detail information to obtain the luminance base layer further comprises the steps of:
carrying out multi-scale filtering processing on the reference image to obtain a series of filtered image information with different scales; and
filtered image information having different scales is synthesized to obtain the luminance base layer.
13. The image processing method as claimed in claim 11, wherein in the step of filtering the reference image by an image detail information-based filter to obtain the luminance base layer, the filter for filtering the reference image based on the image detail information is selected from any one of the group consisting of a bilateral filter, a guided filter and a local edge preserving filter.
14. The image processing method of claim 10, wherein the step of decomposing the reference image to obtain a luminance base layer and a luminance detail layer further comprises the steps of:
processing the reference image by an algorithm for extracting image detail information to obtain the brightness detail layer; and
the reference image is subtracted from the luminance detail layer to obtain the luminance base layer.
15. The image processing method according to any one of claims 10 to 14, wherein the step of reconstructing color information of the color image to be processed based on the luminance mapped image and the reference image further comprises the steps of:
solving a local gain according to the brightness change before and after the reference image mapping, wherein the local gain is expressed by a formula:
wherein L isltmRepresenting the luminance map image, L representing the reference image; and
reconstructing RGB channel information of the color image to be processed according to the local gain, wherein the reconstruction relation is expressed by a formula as follows:
wherein
Max is the number of gray levels of the reference image, wIs the weight factor of the corresponding term.
16. The image processing method according to claim 15, wherein the image processing method further comprises the steps of:
and outputting the reconstructed color image.
17. An image processing system, comprising:
an acquisition module;
a decomposition module;
a processing module;
the acquisition module, the decomposition module, the processing module and the reconstruction module are mutually connected in a communication way, the acquisition module is used for acquiring a color image to be processed and extracting the brightness channel information of the color image to be processed, and the brightness information image is set as a reference image; the decomposition module is connected with the acquisition module in a communication way and is used for decomposing the reference image to obtain a brightness base layer and a brightness detail layer; the processing module is communicably connected to the decomposition module and is used for respectively performing local mapping on the luminance base layer and the luminance detail layer to obtain a luminance mapping image; the reconstruction module is communicably connected to the acquisition module and the processing module and is used for reconstructing RGB channel information of the color image to be processed based on the brightness mapping image and the reference image;
wherein the step of separately mapping the luminance base layer and the luminance detail layer to obtain a luminance mapped image further comprises the steps of:
performing local tone mapping on the luminance base layer, wherein the mapping Curve of the luminance base layer is set as CurveBAnd the luminance base layer Scale curve is ScaleBWherein CurveBIs a cubic spline interpolation function with interpolation points (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleBIs a three-segment polynomial interpolation function, the left and right segments are polynomial interpolation, the middle segment is linear interpolation, the interpolation points (0, a), (b,0), (c,0), (1, d), a, b, c are belonged to[0,1],b≤c,d∈[-1,0]；
Performing local tone mapping on the luminance detail layer, wherein the mapping Curve of the luminance detail layer is set as CurveDAnd the brightness detail layer Scale curve is ScaleDWherein CurveDIs a cubic spline interpolation function with interpolation points (-1,0), (-a, -b), (0,0), (a, b), (1,0), a, b ∈ [0,1 ]](ii) a Curve ScaleDIs a cubic spline interpolation function, the interpolation points (0, a), (b, c), (1, d), a, b, c, d are belonged to [0,1 ∈ ]](ii) a And
obtaining the brightness mapping image, wherein the brightness mapping image is formulated as: l isltm＝wBCurveB(L)ScaleB(B)+wDCurveD(D)ScaleD(B) + L, wherein the LltmRepresenting the luminance map image; w is aB、wDIs the corresponding weighting factor, L is the reference image, B is the luminance base layer and D is the luminance detail layer.
18. The image processing system of claim 17, wherein the image processing system further comprises an output module communicatively coupled to the reconstruction module for outputting the reconstructed color image.
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