Enhanced high-resolution method for visual communication image

Abstract

The invention belongs to the technical field of image data processing, and provides an enhanced high-resolution method for a visual communication image, and the method comprises the steps: obtaining an edge in a visual communication grayscale image; obtaining an information loss factor of the visual communication grayscale image according to the information amount and the texture fineness of the edge analysis visual communication grayscale image; the change of the gray value of the pixel point in the visual communication gray image is analyzed, and a color loss factor of the visual communication gray image is obtained; according to the information loss factor and the color loss factor, obtaining the visual transmission loss of the visual transmission grayscale image; and performing high-resolution enhancement on the visual transmission image, and obtaining the visual transmission image with the optimal resolution according to the visual transmission loss. According to the method, the conditions that textures of characters and patterns in the visual communication image are not clear and color blocks and mottles exist in colors are combined, the accuracy of visual communication loss calculation is improved, and a good reference is provided for subsequent enhancement of high resolution of the visual communication image.

Description

Technical Field

[0001] The present application relates to the technical field of image data processing, and in particular to a method for enhancing high resolution of visually communicated images. Background Art

[0002] Images in visual communication usually refer to the process and results of conveying information or expressing intentions through visual elements (such as images, charts, graphics, etc.). These images can be static or dynamic, conveying complex concepts, emotions or information in a visual form; in order to make the image more clearly show details and edges, thereby improving the visual experience and the effect of information communication, it is necessary to enhance the high resolution of the visual communication image, mainly to enhance the clarity and details of the image.

[0003] Visual communication images usually refer to images used to convey specific information or trigger specific feelings and emotions. Visual communication images may involve various contents and purposes, such as advertising, artwork, design, scientific visualization, etc. The content that needs to be enhanced depends on the specific purpose and design goal of the image. However, due to the internal complexity of 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 purpose and design goal. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an enhanced high-resolution method for visual communication images.

[0005] According to a method for enhancing high resolution of a visual communication image provided by the present application, the method comprises: grayscale the visual communication image to obtain a visual communication grayscale image; Performing edge detection on the visual communication grayscale image to obtain edges in the visual communication grayscale image; Analyzing the information amount and texture fineness of the visually communicated grayscale image according to the edge to obtain an information loss factor of the visually communicated grayscale image; Analyzing the change of the grayscale value of the pixel in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image; Obtaining a visual communication loss of the visual communication grayscale image according to the information loss factor and the color loss factor; The visual communication image is enhanced in high resolution to obtain a visual communication image with optimal resolution according to the visual communication loss.

[0006] In some embodiments of the present invention, edge detection is performed on the visual communication grayscale image to obtain gradient values ​​and gradient directions of pixels in the visual communication grayscale image, and the gradient values ​​and gradient directions are analyzed to obtain the amount of information of the visual communication grayscale image.

[0007] In some embodiments of the present invention, the jaggedness of the edge and the positional relationship between different edges are analyzed to obtain the texture fineness of the visually conveyed grayscale image.

[0008] In some embodiments of the present invention, an edge chain code is constructed for the edge to obtain a chain code value of an edge pixel on the edge, and the difference between the chain code value of the edge pixel and the chain code value of an adjacent edge pixel is analyzed to obtain the jaggedness of the edge.

[0009] In some embodiments of the present invention, the edge is analyzed relative to its nearest edge. Distance and Euclidean distance are used to obtain the positional relationship between the different edges.

[0010] In some embodiments of the present invention, analyzing the change of the grayscale value of the pixel in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image includes: Performing edge detection on the visual communication grayscale image to obtain gradient values ​​of pixel points in the visual communication grayscale image; Taking a pixel point in the visually communicated grayscale image as the center, constructing a neighborhood of the pixel point; Analyzing the variation relationship of the gradient values ​​of the pixels in the neighborhood of the pixel points to obtain the mottle factor of the pixels in the visually conveyed grayscale image; The distribution characteristics of the mottle factors of the pixels in the visual communication grayscale image are analyzed to obtain the color loss factor of the visual communication grayscale image.

[0011] 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 of the Euclidean distances between all the pixels with the closest gradient values ​​in the neighborhood of the pixel points is analyzed to obtain the mottle factor of the pixel points in the visually communicated grayscale image.

[0012] In some embodiments of the present invention, the image pyramid upsampling method is used to perform high-resolution enhancement on the visual communication image, and each upsampling operation enlarges the new visual communication image to an exponential multiple of 4 of the original visual communication image.

[0013] In some embodiments of the present invention, obtaining a visual communication image with an optimal resolution according to the visual communication loss includes: Each time the image pyramid is upsampled, the information content and visual communication loss of the new visual communication image are obtained; 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; A visual communication image with an optimal resolution is obtained according to the information amount difference and the visual communication loss of the new visual communication image.

[0014] In some embodiments of the present invention, edge detection is performed on the visual communication grayscale image using a Sobel edge detection algorithm to obtain edges in the visual communication grayscale image.

[0015] It can be seen from the above embodiments that the method for enhancing high resolution of visually communicated images provided in the embodiments of the present application has the following beneficial effects: The present invention obtains the edge of the visual communication image through an edge detection algorithm, analyzes the amount of information of the image and the fineness of the texture of the image according to the edge of the image, and then obtains the information loss factor of the visual communication image. The information loss factor takes into account the unclear texture of text and patterns caused by low resolution, thereby improving the accuracy of visual communication loss calculation. Since the visual communication image is not only transmitted by text and pattern textures, but also the color in the visual communication image will have color blocks and mottled spots due to the low resolution, the color loss factor of the visual communication image is obtained through the change of pixels in the visual communication image, thereby 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, which provides a good reference for the subsequent enhancement of high resolution of the visual communication image.

[0016] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of the basic flow of a method for enhancing high resolution of visually communicated images provided in an embodiment of the present application; Figure 2 A schematic diagram of the basic flow of a method for analyzing color loss factors of visually conveyed grayscale images provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method for enhancing high resolution of visual communication images proposed by the present invention, its specific implementation, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0021] For the acquired visual communication image, in order to improve the clarity, visual appeal and realism of the visual communication image, it is necessary to enhance the resolution of the acquired visual communication image. Since the visual communication image is relatively complex, the embodiment of the present application arranges the complex internal information to achieve adaptive enhancement of the visual communication image to a high resolution.

[0022] A method for enhancing high resolution of visual communication images provided by this embodiment will be described in detail below with reference to the accompanying drawings.

[0023] See also Figure 1 , which shows the basic process of a method for enhancing high resolution of visual communication images provided by an embodiment of the present invention.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a method for enhancing high resolution of a visual communication image, which specifically includes the following steps: S100: grayscale the visual communication image to obtain a visual communication grayscale image.

[0025] Visual communication images usually refer to images used to convey specific information or trigger specific feelings and emotions. Visual communication images may involve various contents and purposes, such as advertising, artwork, design, scientific visualization, etc. The content that needs to be enhanced depends on the specific purpose and design goal of the image. Obtain visual communication images that need to be enhanced and processed with high resolution. Since high-resolution enhancement processing of visual communication images only requires analysis and processing of their textures, the collected visual communication images are grayed to obtain visual communication grayscale images to improve the efficiency of enhanced high-resolution processing of visual communication images.

[0026] At this point, a visual communication grayscale image is obtained.

[0027] Since the information transmission requirements of different parts of the visual communication image are different, each part of the image needs to be enhanced to different degrees. It is necessary to obtain the information transmission requirements of each part of the visual communication image according to the texture in the visual communication grayscale image, so as to enhance the visual communication image with high resolution. The text information and pattern information in the visual communication image are more important, so the texture composed of the text information and pattern information is analyzed to obtain the visual communication loss in the visual communication image.

[0028] The present invention processes a visual communication grayscale image to obtain a visual communication loss of the visual communication image, including steps S200 to S500.

[0029] S200: performing edge detection on the visual communication grayscale image to obtain edges in the visual communication grayscale image.

[0030] This embodiment needs 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 it is necessary to first obtain the texture in the visual communication image. Therefore, by performing edge detection on the visual communication grayscale image, the edge in the visual communication grayscale image is obtained. Further, the edge detection of the visual communication grayscale image is performed using the Sobel edge detection algorithm to obtain the edge in the visual communication grayscale image. The edge in the visual communication grayscale image is generated by the texture composed of the text information and pattern information in the visual communication grayscale image.

[0031] So far, the edge in the visually communicated grayscale image is obtained, that is, the edge in the visually communicated image is obtained.

[0032] S300: Analyze the information amount and texture fineness of the visually communicated grayscale image according to the edge, and obtain the information loss factor of the visually communicated grayscale image.

[0033] Since the information transmitted by each part of the visual communication image is different, the required enhancement strength for each part of the visual communication image is different, and it is necessary to distinguish according to the amount of information in the visual communication image reflected by the pixels in the visual communication image. At the same time, the texture in the visual communication image can also reflect the amount of information in the visual communication image, so the information loss factor of the visual communication image can be analyzed by analyzing the difference between the amount of information reflected by the pixels and the amount of information reflected by the texture.

[0034] In order to improve the processing efficiency of enhanced high resolution of visual communication images, this embodiment uses visual communication grayscale images as analysis objects to obtain information loss factors. Therefore, this embodiment analyzes the information content and texture fineness of visual communication grayscale images according to edges to obtain the information loss factor of visual communication grayscale images.

[0035] Among them, the information content of the visual communication grayscale image is analyzed. The specific operation method is to use the Sobel edge detection algorithm to perform edge detection on the visual communication grayscale image to obtain the gradient value and gradient direction of the pixel points in the visual communication grayscale image. Analyze the gradient value and gradient direction, that is, classify the gradient values ​​of the pixels in the visual communication grayscale image and the pixels with the same gradient direction into one category, and calculate the proportion of the number of pixels in each category to the total number of pixels. According to the probability distribution of each category of pixels in the visual communication grayscale image, its information entropy is obtained. The information entropy reflects the distribution of each category of pixels. The larger the information entropy value, the more gradient value and gradient direction classifications there are in the visual communication grayscale image, and therefore the larger the information content in the visual communication grayscale image. The information entropy is linearly normalized by norm to obtain the information content of the visual communication grayscale image.

[0036] Since the intuitive expression of the resolution of the visual communication grayscale image is the jagged degree of the visual communication grayscale image texture, the Sobel edge segmentation image of the visual communication grayscale 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 rendering the details of the visual communication image.

[0037] For each pixel in the visual communication grayscale image, if the pixel is located in the text part of the visual communication grayscale image, since the text contains more information, it is necessary to obtain its text coefficient based on the texture of the pixel position. Due to the characteristics of the text composition, the edges of the text part usually have strips, and there are relatively rapid turns at the edges. However, due to the low resolution of the visual communication grayscale image, the boundaries of the visual communication grayscale image are not smooth and natural enough. Therefore, it is necessary to analyze the jaggedness of the edges in the visual communication grayscale image, and analyze the texture fineness of the image in combination with the positional relationship between different edges.

[0038] Therefore, the texture fineness of the visually communicated grayscale image is analyzed. The specific operation method is to analyze the jaggedness of the edge and the positional relationship between different edges to obtain the texture fineness of the visually communicated grayscale image.

[0039] Furthermore, by constructing edge chain codes for the edges, we obtain the chain code values ​​of edge pixels on the edges, and analyze the differences between the chain code values ​​of edge pixels and those of its adjacent edge pixels, that is, we calculate the average of the absolute values ​​of the differences between the chain code values ​​of edge pixels and the chain code values ​​of the two adjacent edge pixels before and after them, and perform norm linear normalization on the average of the absolute values ​​of the differences to obtain the jaggedness of the edge. In addition, we analyze the difference between the edge and its nearest edge. (Dynamic Time Warping) distance and Euclidean distance are used to obtain the positional relationship between different edges. Then, according to the jaggedness of the edge and the analysis of the positional relationship between different edges, the texture fineness of the visually conveyed grayscale image is obtained.

[0040] The calculation formula for the texture fineness of the visually conveyed grayscale image is: ; In the formula, Indicates the texture fineness of the visually conveyed grayscale image; Represents any edge in a visually conveyed grayscale image; Represents the number of edges in a visually conveyed grayscale image; Indicates the visual communication grayscale image with The edge closest to the edge of the strip; Represents the first The serration of the strip edge; Represents the first The edge of a strip and its nearest edge Distance value; Represents the first The Euclidean distance between a strip edge and its nearest edge centroid; Represents the first The average aliasing of the two nearest pixels between the edge of the strip and its nearest edge; Expressed as a natural constant An exponential function with base .

[0041] For visually conveying the jaggedness of each edge in a grayscale image The larger the value, the more burrs there are on the edge. Therefore, the edge will affect the texture in the visual communication grayscale image, thereby affecting the information transmission of the visual communication grayscale image. The smaller the texture fineness of the corresponding visual communication grayscale image, the smaller the texture fineness of the corresponding visual communication grayscale image. Negatively correlated; and for visual communication, the average aliasing between the nearest two pixels of any edge and its nearest edge in a grayscale image is related to the average aliasing between the edge and its nearest edge. Ratio of distance values The larger the value, the Compared to The larger it is, the more similar the edge in the visual communication grayscale image is to its nearest edge, and the jaggedness of its nearest pixel is larger, so the influence of the two nearest pixels on the two edges is more obvious, so the two nearest pixels on the two edges have a greater influence on the edge texture of the visual communication grayscale image, that is, the greater the influence on the information transmission of the visual communication grayscale image, the smaller the texture fineness of the corresponding visual communication grayscale image, so its value is negatively correlated with the texture fineness of the visual communication grayscale image; and for the Euclidean distance between any edge in the visual communication grayscale image and the centroid of its nearest edge The smaller its value is, the smaller the Euclidean distance between edges is. Therefore, the more obvious the impact caused by the jagged edge is, and the smaller the texture fineness of the corresponding visual communication grayscale image is. Therefore, its value is positively correlated with the texture fineness coefficient of the visual communication grayscale image.

[0042] Then, according to the information amount and texture fineness of the visually communicated grayscale image, the information loss factor of the visually communicated grayscale image is obtained. The calculation formula of the information loss factor is: ; In the formula, Indicates the information loss factor of the visually conveyed grayscale image; Indicates the amount of information visually conveyed by a grayscale image; Indicates the texture fineness of the visually conveyed grayscale image; represents the hyperbolic tangent function.

[0043] It indicates the difference between the amount of information visually conveyed by the grayscale image and the fineness of the texture. The larger the value, the greater the difference between the amount of information visually conveyed by the grayscale image and the fineness of the texture, indicating that the more information loss of the grayscale image visually conveyed.

[0044] At this point, the information loss factor of the visually communicated grayscale image is obtained, that is, the information loss factor of the visually communicated image is obtained.

[0045] S400: Analyze the change of the grayscale value of the pixel in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image.

[0046] Step S300 obtains the information loss factor caused by low resolution in the visual communication grayscale image. However, since the visual communication image is not only transmitted by text and pattern textures, the color in the visual communication image will also cause color blocks and spots in the visual communication image due to low resolution. The manifestation of color blocks and spots in the visual communication image due to resolution problems is that some parts of the visual communication image are divided into small blocks and the color transition is unnatural. Therefore, it is necessary to obtain the color loss factor of the visual communication image based on the change in the grayscale value of the rest 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 edge detection cannot detect the color blocks and spots therein. Therefore, the edge obtained by the sobel edge detection is the normal texture in the visual communication image, and it is necessary to analyze the texture of the remaining parts to obtain the color loss factor in the image.

[0047] The color blocks and mottled phenomena in the visual communication images are caused by the low resolution, which leads to the uneven color transition in the visual communication images. The uneven color transition is caused by the uneven change of grayscale values ​​between pixels in the visual communication images. Therefore, the mottled factor of the pixel is obtained by the change of grayscale values ​​of each pixel and its neighboring pixels, and then the color loss factor of the visual communication image is obtained through the mottled factor of the pixel.

[0048] In order to improve the processing efficiency of enhancing high resolution of visual communication images, this embodiment still uses the visual communication grayscale image as the analysis object to obtain the color loss factor. Therefore, this embodiment obtains the color loss factor of the visual communication grayscale image by analyzing the change of the grayscale value of the pixel points in the visual communication grayscale image.

[0049] See also Figure 2 , which shows the basic process of a color loss factor analysis method for visually conveying grayscale images provided by an embodiment of the present invention.

[0050] like Figure 2 As shown, the change of the gray value of the pixel in the visual communication gray image is analyzed to obtain the color loss factor of the visual communication gray image, including: S401: Perform edge detection on the visual communication grayscale image to obtain gradient values ​​of pixel points in the visual communication grayscale image.

[0051] The sobel edge detection algorithm is used to perform edge detection on the visual communication grayscale image to obtain the gradient value of the pixel points in the visual communication grayscale image.

[0052] S402: Constructing a neighborhood of the pixel point with the pixel point in the visually conveyed grayscale image as the center.

[0053] For the pixels in the visual communication grayscale image, a 5×5 neighborhood is constructed with the pixel as the center. For each pixel, in the visual communication grayscale image, the edge can filter out the parts where the color and texture change. Generally, the grayscale value of the pixels on the same side of the edge changes less, and the grayscale value of the pixels on both sides of the edge changes more. The pixels on the same side of the edge usually have color blocks and / or mottled phenomena. Therefore, the changes in the grayscale values ​​of the pixels on the same side of the edge are analyzed to obtain their mottled factors. Therefore, when a 5×5 neighborhood is constructed with the pixel as the center, when the neighborhood of the pixel crosses the edge, the neighborhood of the pixel only retains the pixels on the same side of the edge as the pixel, and the edge pixels and the pixels on the other side of the edge are not included in the neighborhood pixel range.

[0054] S403: Analyze the variation relationship of the gradient value of the pixel point in the neighborhood of the pixel point to obtain the mottle factor of the pixel point in the visually conveyed grayscale image.

[0055] The gradient value of a pixel represents the rate of change of the grayscale value of the pixel. In order to more accurately analyze the change of the grayscale value of a pixel, the change of the gradient value of the pixel can be analyzed, that is, the change of the rate of change of the grayscale value of the pixel can be analyzed. Therefore, this embodiment obtains the mottle factor of the pixel in the visually communicated grayscale image by analyzing the change relationship of the gradient value of the pixel in the neighborhood of the pixel. Furthermore, the mottle factor of the pixel in the visually communicated grayscale image is obtained by analyzing the variance of the gradient values ​​of all pixels in the neighborhood of the pixel, and analyzing the mean of the Euclidean distances between all pixels with the closest gradient values ​​in the neighborhood of the pixel. The calculation formula for the mottle factor is: ; In the formula, Represents the first The mottle factor of each pixel is Indicates the serial number of any pixel in the visually conveyed grayscale image except the edge pixels; Represents any pixel in the neighborhood of a pixel in a visually conveyed grayscale image; Represents the neighborhood of the pixel point and the pixel point The pixel with the closest gradient value; Represents the first The variance of the gradient values ​​of all pixels in the neighborhood of a pixel; Represents the first All pixels in the neighborhood of pixels The pixel with the closest gradient value The mean of the Euclidean distance between the pixels. If the number of pixels closest to its gradient value is greater than one, the pixel with the closest Euclidean distance is selected; represents the hyperbolic tangent function.

[0056] For visually conveying the variance of the gradient values ​​of all pixels in the neighborhood of any pixel in a grayscale image , the larger its value, the more inconsistent the gradient of the pixels in the neighborhood of the pixel point is; and the mean of the Euclidean distances between each pixel in the neighborhood of the pixel point and the pixel point closest to its gradient value is The smaller its value is, the closer the distance between the pixel point and the pixel point closest to its gradient value in the neighborhood of the pixel point in the visual communication grayscale image is; the ratio of the two values ​​is The larger its value is, the more inconsistent the gradient of the pixel in the neighborhood of the pixel in the visually conveyed grayscale image is and the closer the pixel is to the pixel with a similar gradient value. Therefore, it shows that the gradient values ​​in the neighborhood of the pixel are inconsistent but the pixels with similar gradient values ​​are close, indicating that there are mottled pixels in the neighborhood of the pixel. Therefore, it is positively correlated with the mottle factor.

[0057] S404: Analyze the distribution characteristics of the mottle factor of the pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image.

[0058] Analyze the distribution characteristics of the mottle factor of the pixel points in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image. Furthermore, analyze the difference between the mottle factor of the pixel points in the visual communication grayscale image and the mottle factor of its neighborhood pixel points, and combine the mean of the mottle factor of all pixel points in the visual communication grayscale image except the edge pixel points to obtain the color loss factor of the visual communication grayscale image; where the neighborhood pixel point represents the pixel point in the neighborhood of the pixel point, and the neighborhood of the pixel point is a 5×5 neighborhood built with the pixel point as the center. The calculation formula of the color loss factor is: ; In the formula, Indicates the color loss factor of the visually conveyed grayscale image; It represents the mean value of the mottle factor of all pixels except edge pixels in the visually conveyed grayscale image; Represents the first The mean of the absolute values ​​of the differences in the mottle factor between a pixel and its neighboring pixels; Indicates the number of pixels other than edge pixels in a visually conveyed grayscale image; The natural constant An exponential function with base .

[0059] For visual communication, the mean value of the mottle factor of all pixels in the 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, so it is positively correlated with the color loss factor. The mean of the absolute value of the difference between the mottle factor of a pixel and its neighboring pixels The smaller its value is, the closer the mottle factor is in the visual communication grayscale image, and the larger the color loss factor of the corresponding visual communication grayscale image is, so it is negatively correlated with the color loss factor.

[0060] So far, the color loss factor of the visually communicated grayscale image is obtained, that is, the color loss factor of the visually communicated image is obtained.

[0061] S500: Obtaining a visual communication loss of a visual communication grayscale image according to an information loss factor and a color loss factor.

[0062] The visual communication loss of the visual communication grayscale image is the sum of information loss and color loss. Therefore, according to the information loss factor and the color loss factor, the visual communication loss of the visual communication grayscale image is obtained. The calculation formula of visual communication loss is: ; In the formula, Represents the visual communication loss of the visually communicated grayscale image; Indicates the information loss factor of the visually conveyed grayscale image; Represents the color loss factor of the visually conveyed grayscale image.

[0063] At this point, the visual communication loss of the visual communication grayscale image is obtained, that is, the visual communication loss of the visual communication image is obtained.

[0064] S600: Perform high-resolution enhancement on the visual communication image to obtain a visual communication image with optimal resolution according to the visual communication loss.

[0065] The image pyramid upsampling method is used to enhance the high resolution of the visual communication image. For each upsampling operation, the new visual communication image is enlarged to an exponential multiple of 4 of the original visual communication image. In addition, each image pyramid upsampling operation obtains the information volume of the new visual communication image. and visual communication loss , the acquisition method is the method provided by step S100 to step S500; then the information amount of the new visual communication image is calculated The difference between the information content of the new visual communication image and the information content of the original visual communication image is calculated. The absolute value of the difference between ; Then, according to the information difference and the visual communication loss of the new visual communication image, the optimal resolution visual communication image is obtained, specifically, the upsampling operation is repeated. When it is the smallest, it indicates that the visual communication loss of the newly acquired visual communication image is the smallest and there is no difference in the amount of information from the original visual communication image. At this time, the corresponding new visual communication image is a visual communication image with the optimal resolution. Therefore, the enhancement ends and the enhanced visual communication image is obtained.

[0066] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0067] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the article or device including the element.

[0068] Other embodiments of the present invention will be readily apparent to those skilled in the art after considering the specification and practicing the invention invented herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not invented by the present invention.

[0069] It should be understood that the present invention is not limited to the above description, and various modifications and changes may be made without departing from the scope thereof, and various modifications and changes made without departing from the scope thereof are within the protection scope of the present invention.

Claims

1. A method for enhancing high resolution of visual communication images, characterized in that: The method comprises: grayscale the visual communication image to obtain a visual communication grayscale image; Performing edge detection on the visual communication grayscale image to obtain edges in the visual communication grayscale image; Analyzing the information amount and texture fineness of the visually communicated grayscale image according to the edge to obtain an information loss factor of the visually communicated grayscale image; Analyzing the change of the grayscale value of the pixel in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image; Obtaining a visual communication loss of the visual communication grayscale image according to the information loss factor and the color loss factor; The visual communication image is enhanced in high resolution to obtain a visual communication image with optimal resolution according to the visual communication loss.

2. The method for enhancing high resolution of visual communication images according to claim 1, characterized in that: Perform edge detection on the visual communication grayscale image to obtain gradient values ​​and gradient directions of pixels in the visual communication grayscale image, analyze the gradient values ​​and the gradient directions, and obtain information content of the visual communication grayscale image.

3. The method for enhancing high resolution of visual communication images according to claim 1, characterized in that: The jaggedness of the edge and the positional relationship between different edges are analyzed to obtain the texture fineness of the visually conveyed grayscale image.

4. The method for enhancing high resolution of visual communication images according to claim 3, characterized in that: An edge chain code is constructed for the edge to obtain a chain code value of an edge pixel on the edge, and a difference between the chain code value of the edge pixel and the chain code value of an adjacent edge pixel is analyzed to obtain the jaggedness of the edge.

5. The method for enhancing high resolution of visual communication images according to claim 3, characterized in that: Analyze the relationship between the edge and its nearest edge Distance and Euclidean distance are used to obtain the positional relationship between the different edges.

6. The method for enhancing high resolution of visual communication images according to claim 1, characterized in that: Analyzing the change of the grayscale value of the pixel in the visual communication grayscale image to obtain the color loss factor of the visual communication grayscale image includes: Performing edge detection on the visual communication grayscale image to obtain gradient values ​​of pixel points in the visual communication grayscale image; Taking a pixel point in the visually communicated grayscale image as the center, constructing a neighborhood of the pixel point; Analyzing the variation relationship of the gradient values ​​of the pixels in the neighborhood of the pixel points to obtain the mottle factor of the pixels in the visually conveyed grayscale image; The distribution characteristics of the mottle factors of the pixels in the visual communication grayscale image are analyzed to obtain the color loss factor of the visual communication grayscale image.

7. The method for enhancing high resolution of visual communication images according to claim 6, characterized in that: The variance of the gradient values ​​of the pixel points in the neighborhood of the pixel points is analyzed, and the mean 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 mottle factor of the pixel points in the visually communicated grayscale image.

8. The method for enhancing high resolution of visual communication images according to claim 1, characterized in that: The image pyramid upsampling method is used to perform high-resolution enhancement on the visual communication image. For each upsampling operation, the new visual communication image is enlarged to an exponential multiple of 4 of the original visual communication image.

9. The method for enhancing high resolution of visual communication images according to claim 8, characterized in that: According to the visual communication loss, a visual communication image with an optimal resolution is obtained, including: Each time the image pyramid is upsampled, the information content and visual communication loss of the new visual communication image are obtained; 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; A visual communication image with an optimal resolution is obtained according to the information amount difference and the visual communication loss of the new visual communication image.

10. The method for enhancing high resolution of visual communication images according to claim 1, characterized in that: The sobel edge detection algorithm is used to perform edge detection on the visual communication grayscale image to obtain edges in the visual communication grayscale image.

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