An enhanced high-resolution method for visual communication images

Abstract

This application belongs to the technical field of image data processing, and provides an enhanced high-resolution method for visual communication images. By obtaining the edges in the visual communication grayscale image; analyzing the information amount and texture fineness of the visual communication grayscale image according to the edges to obtain the information loss factor of the visual communication grayscale image; and analyzing the change of the grayscale value of the pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image; then obtaining the visual communication loss of the visual communication grayscale image according to the information loss factor and the color loss factor; performing high-resolution enhancement on the visual communication image, and obtaining the visual communication image with the optimal resolution according to the visual communication loss. The method of the present invention combines the situations of unclear textures and the existence of color blocks and mottles in the text and patterns of the visual communication image, improves the accuracy of visual communication loss calculation, and provides a good reference for the subsequent high-resolution enhancement of the visual communication image.

Description

Technical Field

[0001] This application relates to the technical field of image data processing, and in particular to an enhanced high-resolution method for visual communication images. Background Art

[0002] Images in visual communication generally refer to the processes and results of transmitting information or expressing intentions through visual elements (such as images, charts, graphics, etc.). These images can be static or dynamic, and convey complex concepts, emotions, or information in a visual form; in order to enable images to more clearly display details and edges, thereby enhancing the visual experience and the effect of information transmission, it is necessary to enhance the high resolution of visual communication images, mainly to enhance the clarity and details of the images.

[0003] Visual communication images generally refer to images used to convey specific information or trigger specific sensations and emotions. Visual communication images may involve various contents and purposes, such as advertisements, artworks, designs, scientific visualizations, etc. The content to be enhanced depends on the specific use and design goals of the images. However, due to the complexity inside visual communication images, it is difficult to determine the scale of enhanced resolution and it is impossible to perform adaptive image enhancement according to the use and design goals. Summary of the Invention

[0004] To solve the above technical problems, this application provides an enhanced high-resolution method for visual communication images.

[0005] According to an enhanced high-resolution method for visual communication images provided by this application, the method includes:

[0006] Grayscale the visual communication image to obtain a grayscale visual communication image;

[0007] Perform edge detection on the grayscale visual communication image to obtain the edges in the grayscale visual communication image;

[0008] Analyze the information volume and texture fineness of the grayscale visual communication image according to the edges to obtain the information loss factor of the grayscale visual communication image;

[0009] Analyze the change in the grayscale values of the pixel points in the grayscale visual communication image to obtain the color loss factor of the grayscale visual communication image;

[0010] Obtain the visual communication loss of the grayscale visual communication image according to the information loss factor and the color loss factor;

[0011] Perform high-resolution enhancement on the visual communication image, and obtain a visual communication image with the optimal resolution according to the visual communication loss.

[0012] In some embodiments of the present invention, edge detection is performed on the visual communication grayscale image to obtain the gradient value and gradient direction of the pixel points in the visual communication grayscale image, and the gradient value and the gradient direction are analyzed to obtain the information amount of the visual communication grayscale image.

[0013] In some embodiments of the present invention, the serration degree of the edge is analyzed and the positional relationship between different edges is analyzed to obtain the texture fineness of the visual communication grayscale image.

[0014] In some embodiments of the present invention, an edge chain code is constructed for the edge to obtain the chain code value of the edge pixel points on the edge, and the difference between the chain code value of the edge pixel points and the chain code value of its adjacent edge pixel points is analyzed to obtain the serration degree of the edge.

[0015] In some embodiments of the present invention, the distance and Euclidean distance between the edge and its nearest edge are analyzed to obtain the positional relationship between different edges.

[0016] In some embodiments of the present invention, the change of the pixel point gray value in the visual communication grayscale image is analyzed to obtain the color loss factor of the visual communication grayscale image, including:

[0017] Perform edge detection on the visual communication grayscale image to obtain the gradient value of the pixel points in the visual communication grayscale image;

[0018] Taking the pixel points in the visual communication grayscale image as the center, construct the neighborhood of the pixel points;

[0019] Analyze the change relationship of the gradient values of the pixel points in the neighborhood of the pixel points to obtain the mottling factor of the pixel points in the visual communication grayscale image;

[0020] Analyze the distribution characteristics of the mottling factor of the pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image.

[0021] In some embodiments of the present invention, the variance of the gradient values of the pixel points in the neighborhood of the pixel points is analyzed, and the mean value of the Euclidean distances between all the pixel points with the closest gradient values in the neighborhood of the pixel points is analyzed to obtain the mottling factor of the pixel points in the visual communication grayscale image.

[0022] In some embodiments of the present invention, the visual communication image is enhanced with high resolution by using the method of image pyramid upsampling. For each upsampling operation, the new visual communication image is enlarged to an exponential multiple of 4 of the original visual communication image.

[0023] In some embodiments of the present invention, obtaining a visual communication image with optimal resolution according to the visual communication loss includes:

[0024] For each image pyramid upsampling operation, obtain the information amount and visual communication loss of the new visual communication image;

[0025] Calculate the difference between the information amount of the new visual communication image and the information amount of the original visual communication image to obtain the information amount difference;

[0026] Obtain a visual communication image with optimal resolution according to the information amount difference and the visual communication loss of the new visual communication image.

[0027] In some embodiments of the present invention, use the sobel edge detection algorithm to perform edge detection on the visual communication grayscale image to obtain the edges in the visual communication grayscale image.

[0028] As can be seen from the above embodiments, an enhanced high-resolution method for a visual communication image provided by the embodiments of the present application has the following beneficial effects:

[0029] The present invention obtains the edges of the visual communication image through the edge detection algorithm, analyzes the information amount and texture fineness of the image according to the edges of the image, and then obtains the information loss factor of the visual communication image. The information loss factor takes into account the problem of unclear textures of text and patterns caused by low resolution, and improves the accuracy of visual communication loss calculation. Since the visual communication image is not only transmitted by the textures of text and patterns, but also the colors in the visual communication image may have color blocks and mottled situations due to low resolution, the color loss factor of the visual communication image is obtained through the change of pixel points in the visual communication image, further improving the accuracy of visual communication loss calculation. Finally, the visual communication loss of the visual communication image is obtained according to the information loss factor and the color loss factor, providing a good reference for the subsequent enhancement of the high resolution of the visual communication image.

[0030] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1Schematic diagram of the basic process of an enhanced high-resolution method for visual communication images provided by an embodiment of the present application;

[0033] Figure 2 Schematic diagram of the basic process of a color loss factor analysis method for visual communication grayscale images provided by an embodiment of the present application. Detailed implementation manners

[0034] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of an enhanced high-resolution method for visual communication images proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0036] For the obtained visual communication images, in order to improve the clarity, visual appeal, and realism of the visual communication images, it is necessary to perform enhanced resolution processing on the obtained visual communication images. Since visual communication images are relatively complex, the embodiments of the present application adaptively perform enhanced high-resolution on visual communication images according to the complex internal information arrangement.

[0037] The following will, in conjunction with the accompanying drawings, introduce in detail an enhanced high-resolution method for visual communication images provided by this embodiment.

[0038] Please refer to Figure 1 , which shows the basic process of an enhanced high-resolution method for visual communication images provided by an embodiment of the present invention.

[0039] As Figure 1 shown, an enhanced high-resolution method for visual communication images provided by an embodiment of the present invention specifically includes the following steps:

[0040] S100: Gray-scale the visual communication image to obtain a visual communication grayscale image.

[0041] Visual communication images generally refer to images used to convey specific information or trigger specific sensations and emotions. Visual communication images may involve various contents and purposes, such as advertisements, artworks, designs, scientific visualizations, etc. The content to be enhanced depends on the specific use and design goals of the image. Obtain visual communication images that need to be enhanced with high resolution. Since only the texture of the visual communication image needs to be analyzed and processed for high-resolution enhancement, the collected visual communication image is grayscale processed to obtain a visual communication grayscale image, so as to improve the efficiency of high-resolution enhancement processing of the visual communication image.

[0042] So far, a visual communication grayscale image has been obtained.

[0043] Since the information transmission requirements of different parts in the visual communication image are different, resulting in different degrees of enhancement required for each part in the image, it is necessary to obtain the information transmission requirements of each part in the visual communication image according to the texture in the visual communication grayscale image, so as to perform high-resolution enhancement operations on the visual communication image. The text information and pattern information in the visual communication image are relatively important. Therefore, the texture composed of the text information and pattern information is analyzed to obtain the visual communication loss in the visual communication image.

[0044] The process of the present invention for processing the visual communication grayscale image to obtain the visual communication loss of the visual communication image includes steps S200 to S500.

[0045] S200: Perform edge detection on the visual communication grayscale image to obtain the edges in the visual communication grayscale image.

[0046] In this embodiment, it is necessary to obtain the information transmission requirements of each part in the visual communication image according to the texture in the visual communication grayscale image. Therefore, it is first necessary to obtain the texture in the visual communication image. Therefore, by performing edge detection on the visual communication grayscale image, the edges in the visual communication grayscale image are obtained. Further, the sobel edge detection algorithm is used to perform edge detection on the visual communication grayscale image to obtain the edges in the visual communication grayscale image. The edges in the visual communication grayscale image are generated by the texture composed of the text information and pattern information in the visual communication grayscale image.

[0047] So far, the edges in the visual communication grayscale image have been obtained, that is, the edges in the visual communication image have been obtained.

[0048] S300: Analyze the information amount and texture fineness of the visual communication grayscale image according to the edges to obtain the information loss factor of the visual communication grayscale image.

[0049] Since the information conveyed by each part of the visual communication image is different, the enhancement intensity required for each part of the visual communication image is not the same, and it needs to be distinguished according to the amount of information in the visual communication image reflected by the pixel points. At the same time, the texture in the visual communication image can also reflect the amount of information in the visual communication image. Therefore, the information loss factor of the visual communication image can be analyzed by analyzing the difference between the amount of information reflected by the pixel points and the amount of information reflected by the texture.

[0050] In order to improve the processing efficiency of enhancing the high resolution of the visual communication image, in this embodiment, the visual communication gray image is taken as the analysis object to obtain the information loss factor. Therefore, in this embodiment, according to the edge, the amount of information and the texture fineness of the visual communication gray image are analyzed to obtain the information loss factor of the visual communication gray image.

[0051] Among them, to analyze the amount of information in the visual communication gray image, the specific operation method is to use the sobel edge detection algorithm to perform edge detection on the visual communication gray image to obtain the gradient value and gradient direction of the pixel points in the visual communication gray image. Analyze the gradient value and gradient direction, that is, classify the pixel points with the same gradient value and gradient direction in the visual communication gray image into one category, and calculate the proportion of the number of pixel points in each category to the total number of pixel points. Obtain its information entropy according to the probability distribution of each category of pixel points in the visual communication gray image. The information entropy reflects the distribution of each category of pixel points. The larger the information entropy value, the more classification numbers of the gradient value and gradient direction in the visual communication gray image, so it indicates that the amount of information in the visual communication gray image is larger. The information entropy is linearly normalized by norm to obtain the amount of information in the visual communication gray image.

[0052] Since the intuitive manifestation of the resolution of the visual communication gray image is the jagged degree of the texture of the visual communication gray image, the sobel edge segmentation image of the visual communication gray image is obtained. The sobel edge segmentation image contains all the strong and weak edge information in the visual communication image. The role of the strong edge is to outline the image contour, and the weak edge information may be used for the detail rendering of the visual communication image.

[0053] For each pixel point in the visual communication gray image, if the pixel point is located in the text part of the visual communication gray image, since there is a lot of information in the text, the text coefficient needs to be obtained according to the texture at the position of the pixel point. Due to the characteristics of the composition of the text, there are usually strip parts at the edges of the text part, and there are relatively rapid turns at the edges. However, due to the low resolution of the visual communication gray image, the boundary of the visual communication gray image is not smooth and natural enough. Therefore, it is necessary to analyze the jagged degree of the edges in the visual communication gray image and combine the positional relationship between different edges to analyze the texture fineness of the image.

[0054] Therefore, to analyze the texture fineness of a visual communication grayscale image, the specific operation method is to analyze the jaggedness of the edges and the positional relationship between different edges to obtain the texture fineness of the visual communication grayscale image.

[0055] Furthermore, by constructing an edge chain code for the edge, obtaining the chain code values of the edge pixels on the edge, and analyzing the differences between the chain code values of the edge pixels and the chain code values of their adjacent edge pixels, that is, calculating the average value of the absolute values of the two differences between the chain code value of the edge pixel and the chain code values of the two adjacent edge pixels before and after it, and performing norm linear normalization processing on the average value of the absolute values of the differences to obtain the jaggedness of the edge. And, analyze the (dynamic time warping) distance and Euclidean distance between the edge and its nearest edge to obtain the positional relationship between different edges. Furthermore, based on the jaggedness of the edge and the analysis of the positional relationship between different edges, the texture fineness of the visual communication grayscale image is obtained.

[0056] The calculation formula for the texture fineness of the visual communication grayscale image is:

[0057] ;

[0058] In the formula, represents the texture fineness of the visual communication grayscale image; represents any one edge in the visual communication grayscale image; represents the number of edges in the visual communication grayscale image; represents the edge in the visual communication grayscale image that is closest to the th edge; represents the th edge in the visual communication grayscale image; represents the jaggedness of the th edge in the visual communication grayscale image; represents the distance value between the th edge in the visual communication grayscale image and its nearest edge; represents the Euclidean distance between the th edge in the visual communication grayscale image and the centroid of its nearest edge; represents the average jaggedness of the two nearest pixels between the th edge in the visual communication grayscale image and its nearest edge;

[0059] For the jaggedness of each edge in the visual communication grayscale image , the larger its value, the more burrs on the edge. Therefore, the edge will affect the texture in the visually conveyed grayscale image, thus affecting the information transmission of the visually conveyed grayscale image. The smaller the texture fineness of the corresponding visually conveyed grayscale image, so its texture fineness is negatively correlated with that of the visually conveyed grayscale image; and for the average serration degree between any two nearest pixels of any edge and its nearest edge in the visually conveyed grayscale image and the ratio of the distance value between the edge and its nearest edge , the larger its value indicates compared with is larger, indicating that the edge in the visually conveyed grayscale image is more similar to its nearest edge, and the serration degree of its nearest pixel points is larger. Therefore, the influence of the two nearest pixel points on the two edges is more obvious. Therefore, the two nearest pixel points on the two edges have a greater impact on the edge texture of the visually conveyed grayscale image, that is, a greater impact on the information transmission of the visually conveyed grayscale image. The smaller the texture fineness of the corresponding visually conveyed grayscale image, so its value is negatively correlated with the texture fineness of the visually conveyed grayscale image; and for the Euclidean distance between any edge and the centroid of its nearest edge in the visually conveyed grayscale image

[0060] , the smaller its value indicates that the Euclidean distance between the edges is smaller. Therefore, the influence caused by the serrated shape of the edge is more obvious, and the smaller the texture fineness of the corresponding visually conveyed grayscale image. Therefore, its value is positively correlated with the texture fineness coefficient of the visually conveyed grayscale image.

[0060] Then, according to the information amount and texture fineness of the visually conveyed grayscale image, the information loss factor of the visually conveyed grayscale image is obtained. The calculation formula of the information loss factor is:

[0061] ;

[0062] In the formula, represents the information loss factor of the visually conveyed grayscale image; represents the information amount of the visually conveyed grayscale image; represents the texture fineness of the visually conveyed grayscale image; represents the hyperbolic tangent function.

[0063] represents the difference between the information amount and texture fineness of the visually conveyed grayscale image. The larger its value, the greater the difference between the information amount and texture fineness of the visually conveyed grayscale image, indicating more information loss in the visually conveyed grayscale image.

[0064] So far, the information loss factor of the visually conveyed grayscale image has been obtained, that is, the information loss factor of the visually conveyed image has been obtained.

[0065] S400: Analyze the change in the gray value of pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image.

[0066] In step S300, the information loss factor caused by the low resolution in the visual communication grayscale image is obtained. However, since the visual communication image not only transmits information through text and pattern textures, but also for the color in the visual communication image, there will be cases of color blocks and mottling in the visual communication image due to low resolution. The manifestations of color blocks and mottling in the visual communication image caused by the resolution problem are: some parts of the visual communication image are like being divided into small pieces and the color transition is not natural. Therefore, it is necessary to obtain the color loss factor of the visual communication image according to the change in the gray value of the remaining parts of the visual communication image except the edge part. Since the color problem of the visual communication image caused by low resolution is not particularly obvious in the visual communication image, the color blocks and mottling cannot be detected by edge detection. Therefore, the edges obtained by sobel edge detection are the normal textures in the visual communication image, and it is necessary to analyze the textures of the remaining parts to obtain the color loss factor in the image.

[0067] For the color blocks and mottling phenomena in the color of the visual communication image, they are caused by the uneven color transition in the visual communication image due to low resolution, and the uneven color transition is due to the relatively uneven change in the gray values between pixel points in the visual communication image; therefore, the mottling factor of pixel points is obtained through the change in the gray values between each pixel point and its neighboring pixel points, and then the color loss factor of the visual communication image is obtained through the mottling factor of pixel points.

[0068] In order to improve the processing efficiency of enhancing the high resolution of the visual communication image, in this embodiment, the visual communication grayscale image is still used as the analysis object to obtain the color loss factor. Therefore, in this embodiment, the color loss factor of the visual communication grayscale image is obtained by analyzing the change in the gray value of pixel points in the visual communication grayscale image.

[0069] Please refer to Figure 2 , which shows the basic process of a method for analyzing the color loss factor of a visual communication grayscale image provided by an embodiment of the present invention.

[0070] As Figure 2 shown, analyzing the change in the gray value of pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image includes:

[0071] S401: Perform edge detection on the visual communication grayscale image to obtain the gradient value of pixel points in the visual communication grayscale image.

[0072] Edge detection is performed on the visual communication grayscale image using the Sobel edge detection algorithm to obtain the gradient values of the pixel points in the visual communication grayscale image.

[0073] S402: Taking the pixel points in the visual communication grayscale image as the center, construct the neighborhood of the pixel points.

[0074] For the pixel points in the visual communication grayscale image, a 5×5 neighborhood is constructed with the pixel points as the center. For each pixel point, in the visual communication grayscale image, the edge can filter out the parts where the color and texture change. Generally, the gray values of the pixel points on the same side of the edge change less, while the gray values of the pixel points on both sides of the edge change more. And the pixel points on the same side of the edge usually show color blocks and / or mottled phenomena. Therefore, analyze the change of the gray values of the pixel points on the same side of the edge to obtain its mottling factor. So when constructing a 5×5 neighborhood with the pixel points as the center, when the neighborhood of the pixel points crosses the edge, the neighborhood of the pixel points only retains the pixel points on the same side of the edge as the pixel points, and the edge pixel points and the pixel points on the other side of the edge are not within the range of the neighborhood pixel points.

[0075] S403: Analyze the change relationship of the gradient values of the pixel points in the neighborhood of the pixel points to obtain the mottling factor of the pixel points in the visual communication grayscale image.

[0076] The gradient value of a pixel point represents the change rate of the gray value of the pixel point. To more accurately analyze the change of the gray value of the pixel point, the change of the gradient value of the pixel point can be analyzed, that is, analyze the change of the change rate of the gray value of the pixel point. Therefore, in this embodiment, by analyzing the change relationship of the gradient values of the pixel points in the neighborhood of the pixel points, the mottling factor of the pixel points in the visual communication grayscale image is obtained. Further, by analyzing the variance of the gradient values of all pixel points in the neighborhood of the pixel points, and analyzing the mean value of the Euclidean distances between the pixel points with the closest gradient values in the neighborhood of the pixel points, the mottling factor of the pixel points in the visual communication grayscale image is obtained. The calculation formula of the mottling factor is:

[0077] ;

[0078] In the formula, represents the mottling factor of the th pixel point in the visual communication grayscale image, represents the serial number of any pixel point except the edge pixel points in the visual communication grayscale image; represents any pixel point in the neighborhood of the pixel points in the visual communication grayscale image; represents the pixel point in the neighborhood of the pixel points that is closest to the pixel point in terms of gradient value; represents the The variance of the gradient values of all the pixel points in the neighborhood of a pixel point; represents the th pixel point in the visual communication grayscale image, and for all the pixel points in the neighborhood of the pixel point, the mean of the Euclidean distances between the pixel points and the pixel points with the closest gradient values to them. It should be noted that if the number of pixel points with the closest gradient values to a pixel point is greater than one, select the pixel point with the closest Euclidean distance; represents the hyperbolic tangent function.

[0079] For the variance of the gradient values of all the pixel points in the neighborhood of any pixel point in the visual communication grayscale image , the larger its value, the more inconsistent the gradients of the pixel points inside the neighborhood of the pixel point are; and for the mean of the Euclidean distances between each pixel point inside the neighborhood of the pixel point and the pixel point with the closest gradient value to it, the smaller its value, the closer the distance between the pixel points in the neighborhood of the pixel point in the visual communication grayscale image and the pixel points with the closest gradient values to them; the ratio of the two values , the larger its value, the more inconsistent the gradients of the pixel points in the neighborhood of the pixel point in the visual communication grayscale image are and the closer the distances between the pixel points with similar gradient values to the pixel point are. Therefore, it indicates that the pixel points with inconsistent but similar gradient values in the neighborhood of the pixel point are close to each other, indicating that there are mottled situations among the pixel points in the neighborhood of the pixel point. Therefore, it has a positive correlation with the mottle factor.

[0080] S404: Analyze the distribution characteristics of the mottle factors of the pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image.

[0081] Analyze the distribution characteristics of the mottle factors of the pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image. Further, analyze the differences between the mottle factors of the pixel points in the visual communication grayscale image and the mottle factors of their neighboring pixel points, and combine the mean of the mottle factors of all the pixel points except the edge pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image; where the neighboring pixel points refer to the pixel points in the neighborhood of the pixel point, and the neighborhood of the pixel point is a 5×5 neighborhood constructed with the pixel point as the center. The calculation formula for the color loss factor is:

[0082] ;

[0083] In the formula, represents the color loss factor of the visual communication grayscale image; represents the mean of the mottle factors of all the pixel points except the edge pixel points in the visual communication grayscale image; represents the mean of the absolute value of the difference in the mottling factor between the th pixel point in the visual communication grayscale image and its neighboring pixel points; represents the number of pixel points in the visual communication grayscale image except for the edge pixel points; is the exponential function with the natural constant as the base.

[0084] For the mean of the mottling factors of all pixel points in the visual communication grayscale image , the larger its value, the more the color in the visual communication grayscale image is affected by low pixels, and the larger the color loss factor of the corresponding visual communication grayscale image. Therefore, it has a positive correlation with the color loss factor. For the mean of the absolute value of the difference in the mottling factor between a pixel point and its neighboring pixel points , the smaller its value, the closer the mottling factors in the visual communication grayscale image are, and the larger the color loss factor of the corresponding visual communication grayscale image. Therefore, it has a negative correlation with the color loss factor.

[0085] So far, the color loss factor of the visual communication grayscale image has been obtained, that is, the color loss factor of the visual communication image has been obtained.

[0086] S500: According to the information loss factor and the color loss factor, obtain the visual communication loss of the visual communication grayscale image.

[0087] The visual communication loss of the visual communication grayscale image is the sum of the information loss and the color loss. Therefore, according to the information loss factor and the color loss factor, obtain the visual communication loss of the visual communication grayscale image. The calculation formula for the visual communication loss is:

[0088] ;

[0089] In the formula, represents the visual communication loss of the visual communication grayscale image; represents the information loss factor of the visual communication grayscale image; represents the color loss factor of the visual communication grayscale image.

[0090] So far, the visual communication loss of the visual communication grayscale image has been obtained, that is, the visual communication loss of the visual communication image has been obtained.

[0091] S600: Perform high-resolution enhancement on the visual communication image, and obtain the visual communication image with the optimal resolution according to the visual communication loss.

[0092] The method of upsampling the image pyramid is used to enhance the visual communication image with high resolution. In each upsampling operation, the new visual communication image is enlarged by an exponential multiple of 4 of the original visual communication image. Moreover, in each image pyramid upsampling operation, the information amount of the new visual communication image is obtained. and the visual communication loss , and the obtaining method is the method provided by steps S100 to S500; then calculate the information amount of the new visual communication image and the difference between the information amount of the original visual communication image, that is, calculate the absolute value of the difference between the information amount of the new visual communication image and the information amount of the original visual communication image to obtain the information amount difference ; then, according to the information amount difference and the visual communication loss of the new visual communication image, obtain the visual communication image with the optimal resolution. Specifically, repeatedly perform the upsampling operation. When is the smallest, it indicates that the visual communication loss amount of the obtained new visual communication image is the smallest and there is no difference from the information amount of the original visual communication image. At this time, the corresponding new visual communication image is the visual communication image with the optimal resolution. Therefore, the enhancement ends and the enhanced visual communication image is obtained.

[0093] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0094] It should be noted that unless otherwise specified and limited, terms such as "including", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the said element.

[0095] Those skilled in the art will easily think of other implementation schemes of the present invention after considering the specification and practicing the invention here. This application aims to cover any variations, uses or adaptation changes of the present invention, and these variations, uses or adaptation changes follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field of the present invention that the present invention has not invented.

[0096] It should be understood that the present invention is not limited to the above description, and various modifications and changes can be made without departing from its scope, and all such modifications and changes made without departing from its scope are within the protection scope of the present invention.

Claims

1. An enhanced high-resolution method for visual communication images, characterized in that, The method includes: Grayscale the visual communication image to obtain a grayscale visual communication image; Perform edge detection on the grayscale visual communication image to obtain the edges in the grayscale visual communication image; Analyze the information quantity and texture fineness of the grayscale visual communication image according to the edges to obtain the information loss factor of the grayscale visual communication image; Analyze the change of the grayscale values of the pixel points in the grayscale visual communication image to obtain the color loss factor of the grayscale visual communication image; Obtain the visual communication loss of the grayscale visual communication image according to the information loss factor and the color loss factor; Perform high-resolution enhancement on the visual communication image and obtain the visual communication image with the optimal resolution according to the visual communication loss; Among them, analyzing the texture fineness of the grayscale visual communication image includes: ； In the formula, represents the texture fineness of the visual communication grayscale image; represents any one edge in the visual communication grayscale image; represents the number of edges in the visual communication grayscale image; represents the edge closest to the m-th edge in the visual communication grayscale image; represents the serration degree of the m-th edge in the visual communication grayscale image; represents the DTW distance value between the m-th edge and its closest edge in the visual communication grayscale image; represents the Euclidean distance between the m-th edge and the centroid of its closest edge in the visual communication grayscale image; represents the average serration degree of the two closest pixel points among the m-th edge and its closest edge in the visual communication grayscale image; exp represents the exponential function with the natural constant e as the base.

2. The enhanced high-resolution method for visual communication images according to claim 1, characterized in that, Perform edge detection on the grayscale visual communication image to obtain the gradient values and gradient directions of the pixel points in the grayscale visual communication image, and analyze the gradient values and the gradient directions to obtain the information quantity of the grayscale visual communication image.

3. The enhanced high-resolution method for visual communication images according to claim 1, characterized in that Construct an edge chain code for the edge to obtain the chain code values of the edge pixel points on the edge, and analyze the difference between the chain code values of the edge pixel points and the chain code values of their adjacent edge pixel points to obtain the jaggedness of the edge.

4. The enhanced high-resolution method for visual communication images according to claim 1, wherein, Analyze the distance and Euclidean distance between the described edge and its nearest edge to obtain the positional relationship between different edges. ​ 5. The enhanced high-resolution method for visual communication images according to claim 1, wherein Analyzing the change of the grayscale values of the pixel points in the grayscale visual communication image to obtain the color loss factor of the grayscale visual communication image includes: Perform edge detection on the grayscale visual communication image to obtain the gradient values of the pixel points in the grayscale visual communication image; Construct a neighborhood of the pixel point with the pixel point in the grayscale visual communication image as the center; Analyze the change relationship of the gradient values of the pixel points in the neighborhood of the pixel point to obtain the mottling factor of the pixel points in the grayscale visual communication image; Analyze the distribution characteristics of the mottling factor of the pixel points in the grayscale visual communication image to obtain the color loss factor of the grayscale visual communication image.

6. The enhanced high-resolution method for visual communication images according to claim 5, wherein Analyze the variance of the gradient values of the pixel points in the neighborhood of the pixel point and analyze the mean value of the Euclidean distances between the pixel points with the closest gradient values in the neighborhood of the pixel point to obtain the mottling factor of the pixel points in the grayscale visual communication image.

7. The enhanced high-resolution method for visual communication images according to claim 1, wherein, Perform high-resolution enhancement on the visual communication image by using the method of image pyramid upsampling. For each upsampling operation, the new visual communication image is enlarged to an exponential multiple of 4 of the original visual communication image.

8. The enhanced high-resolution method for visual communication images according to claim 7, characterized in that, Obtaining the visual communication image with the optimal resolution according to the visual communication loss includes: For each image pyramid upsampling operation, obtain the information quantity and visual communication loss of the new visual communication image; Calculate the difference between the information quantity of the new visual communication image and the information quantity of the original visual communication image to obtain the information quantity difference; Obtain the visual communication image with the optimal resolution according to the information quantity difference and the visual communication loss of the new visual communication image.

9. The enhanced high-resolution method for visual communication images according to claim 1, characterized in that, Use the sobel edge detection algorithm to perform edge detection on the grayscale visual communication image to obtain the edges in the grayscale visual communication image.

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