Image processing method, image processing device, and readable storage medium

Abstract

An image processing method, an image processing device, and a non-transiently readable storage medium. The image processing method includes: obtaining an input image; obtaining at least two intermediate images based on the input image; and obtaining an output image based on the at least two intermediate images. Each of the at least two intermediate images corresponds to a single type of image content, while different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes. Based on this image processing method, by applying different color filtering processes to the at least two different intermediate images of the input image, the different requirements of different types of image content within the same image are met, the color filtering effect of the entire image is improved, and the problem of poor color filtering effects caused by using a single color filtering algorithm in complex scenes is resolved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to an image processing method, an image processing apparatus, and a non-transitory readable storage medium. Background Art

[0002] Current image processing technologies include color filtering. Typically, color filtering converts a color image in the RGB color space into an image containing only a limited number of colors using certain mapping rules. Common three-color filtering algorithms convert a color image into a red, black, and white image, using the sparsity of the colors to represent the image's brightness, depth, and other color variations, thereby preserving the visual information of the original color image. The better the color filtering algorithm, the closer the processed result is to the original color image. Summary of the Invention

[0003] At least one embodiment of the present disclosure provides an image processing method, comprising: obtaining an input image; obtaining at least two intermediate images based on the input image; and obtaining an output image based on the at least two intermediate images. Each of the at least two intermediate images corresponds to a single type of image content, while different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes.

[0004] For example, in the image processing method provided in at least one embodiment of the present disclosure, obtaining the at least two intermediate images based on the input image includes: obtaining at least two preliminary images based on the input image; and obtaining the at least two intermediate images based on the at least two preliminary images. Each of the at least two preliminary images corresponds to a single type of image content, and the at least two preliminary images correspond one-to-one to the at least two intermediate images.

[0005] For example, in the image processing method provided by at least one embodiment of the present disclosure, the at least two preliminary images include at least two of a first preliminary image, a second preliminary image, a third preliminary image, and a fourth preliminary image. The first preliminary image corresponds to text-type image content, the second preliminary image corresponds to portrait-type image content, the third preliminary image corresponds to geometric-type image content, and the fourth preliminary image corresponds to background-type image content.

[0006] For example, in the image processing method provided by at least one embodiment of the present disclosure, based on the input image, obtaining the at least two preliminary images includes: performing text detection on the input image to obtain the first preliminary image; performing portrait detection on the input image to obtain the second preliminary image; and / or performing geometric figure detection on the input image to obtain the third preliminary image.

[0007] For example, in the image processing method provided by at least one embodiment of the present disclosure, obtaining the at least two preliminary images based on the input image includes: obtaining the fourth preliminary image based on the first preliminary image, the second preliminary image, and the third preliminary image.

[0008] For example, in the image processing method provided by at least one embodiment of the present disclosure, the at least two intermediate images are obtained based on the at least two preliminary images, including: obtaining a first intermediate image based on the first preliminary image and the input image after text color filtering; obtaining a second intermediate image based on the second preliminary image and the input image after portrait color filtering; obtaining a third intermediate image based on the third preliminary image and the input image after geometric figure color filtering; and / or obtaining a fourth intermediate image based on the fourth preliminary image and the input image after background color filtering.

[0009] For example, in the image processing method provided in at least one embodiment of the present disclosure, obtaining the output image based on the at least two intermediate images includes: merging the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image to obtain the output image.

[0010] For example, in the image processing method provided by at least one embodiment of the present disclosure, merging the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image to obtain the output image includes: in response to the absence of overlapping parts among the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image, adding corresponding pixel values in the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image to obtain the output image.

[0011] For example, in the image processing method provided by at least one embodiment of the present disclosure, the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image are merged to obtain the output image, including: in response to the existence of an overlapping portion between the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image, the pixel value of the overlapping portion is based on the intermediate image with the highest priority among the at least two intermediate images including the overlapping portion.

[0012] For example, in the image processing method provided by at least one embodiment of the present disclosure, the priority ranking of the first intermediate image, the second intermediate image, the third intermediate image and the fourth intermediate image is the first intermediate image > the second intermediate image > the third intermediate image > the fourth intermediate image.

[0013] For example, in the image processing method provided by at least one embodiment of the present disclosure, at least two preliminary images are obtained based on the input image, including: in response to the input image being an editable vector image with at least two layers, the at least two layers are respectively used as the at least two preliminary images, wherein each of the at least two layers corresponds to a single type of image content.

[0014] For example, in the image processing method provided by at least one embodiment of the present disclosure, the at least two intermediate images are obtained based on the at least two preliminary images, including: performing corresponding color filtering processing on each of the at least two preliminary images to obtain the at least two intermediate images.

[0015] For example, in the image processing method provided by at least one embodiment of the present disclosure, each of the at least two preliminary images is a binary image.

[0016] For example, in the image processing method provided by at least one embodiment of the present disclosure, the different color filtering processes at least include using different color filtering parameters.

[0017] At least one embodiment of the present disclosure further provides an image processing device comprising: an input module, an acquisition module, and a processing module. The input module is configured to acquire an input image. The acquisition module is configured to acquire at least two intermediate images based on the input image. The processing module is configured to acquire the output image based on the at least two intermediate images. Each of the at least two intermediate images corresponds to a single type of image content, and different intermediate images correspond to different color filtering processes.

[0018] At least one embodiment of the present disclosure further provides another image processing apparatus, comprising: a processor and a memory. The memory includes one or more computer program modules. The one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for performing the image processing method described in any of the above embodiments.

[0019] At least one embodiment of the present disclosure further provides a non-transitory readable storage medium having computer instructions stored thereon, wherein the computer instructions, when executed by a processor, perform the image processing method described in any of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments will be briefly introduced below. Obviously, the drawings described below only relate to some embodiments of the present disclosure, rather than limiting the present disclosure.

[0021] Figure 1A flowchart of an image processing method provided by at least one embodiment of the present disclosure;

[0022] Figure 2 An operational flow chart of an image processing method provided by at least one embodiment of the present disclosure;

[0023] Figure 3 A schematic diagram of an image processing method provided by at least one embodiment of the present disclosure;

[0024] Figure 4 A schematic diagram of a geometric figure detection algorithm provided by at least one embodiment of the present disclosure;

[0025] Figure 5 A schematic diagram of a text detection algorithm provided by at least one embodiment of the present disclosure;

[0026] Figure 6 A schematic diagram of a portrait detection algorithm provided by at least one embodiment of the present disclosure;

[0027] Figure 7 A schematic diagram of a text color filtering algorithm provided for at least one embodiment of the present disclosure;

[0028] Figure 8 A schematic diagram of a portrait color filtering algorithm provided by at least one embodiment of the present disclosure;

[0029] Figure 9 A schematic diagram of a geometric figure color filtering algorithm provided by at least one embodiment of the present disclosure;

[0030] Figure 10 A schematic diagram of a natural image color filtering algorithm provided by at least one embodiment of the present disclosure;

[0031] Figure 11 A schematic block diagram of an image processing method provided by at least one embodiment of the present disclosure;

[0032] Figure 12 A schematic block diagram of another image processing method provided by at least one embodiment of the present disclosure;

[0033] Figure 13 A schematic block diagram of an image processing device provided by at least one embodiment of the present disclosure;

[0034] Figure 14 A schematic block diagram of another image processing device provided for at least one embodiment of the present disclosure;

[0035] Figure 15 A schematic block diagram of another image processing device provided for at least one embodiment of the present disclosure;

[0036] Figure 16 A schematic block diagram of a non-transitory readable storage medium provided for at least one embodiment of the present disclosure; and

[0037] Figure 17 A schematic block diagram of an electronic device provided for at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0038] To make the purpose, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0039] Flowcharts are used in this disclosure to illustrate the operations performed by the systems according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the various steps may be processed in reverse order or simultaneously, as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0040] Unless otherwise defined, the technical or scientific terms used in this disclosure should have the usual meanings understood by people with ordinary skills in the field to which this disclosure belongs. The words "first", "second" and similar words used in this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "an" or "the" do not indicate a quantity limitation, but rather indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0041] Existing image color conversion algorithms for e-ink displays typically transform an image based on certain color mapping rules and error diffusion algorithms, using available similar colors and pixel density to represent variations in color, texture, and grayscale. However, in real-world applications, a single conversion algorithm is insufficiently adaptable to complex scenarios. For example, products like table signs used in conference settings primarily feature simple graphic elements such as text and tables. In these scenarios, binary images with clear outlines and distinct boundaries better meet user needs. Products like bus handles and smart badges require primarily color-based advertisements and complex content such as ID photos. In these scenarios, a color filtering algorithm is required to maximize the image's gradient textures and facial details using a diffusion algorithm. Furthermore, advertisements and badges often include relevant text descriptions, which also require the same rendering effects as the previous scenario. When these two conflicting requirements coexist in the same scenario, a single algorithm cannot meet them. Therefore, a color filtering algorithm solution suitable for more complex scenarios is needed.

[0042] Generally, although conventional color filtering algorithms for processing natural images have good effects, when processing other types of image content (for example, graphics, text, portraits, etc.), they are prone to problems such as aliasing and particles that affect the appearance.

[0043] At least in order to overcome the above-mentioned technical problems, at least one embodiment of the present disclosure provides an image processing method, which includes: obtaining an input image; based on the input image, obtaining at least two intermediate images; based on the at least two intermediate images, obtaining an output image, each of the at least two intermediate images corresponds to a single type of image content, different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes.

[0044] Accordingly, at least one embodiment of the present disclosure further provides an image processing device and a non-transitory readable storage medium corresponding to the above-mentioned image processing method.

[0045] Through the image processing method provided by at least one embodiment of the present disclosure, the input image can be classified and processed, for example, divided into at least two intermediate images of different types, and corresponding color filtering processing can be adaptively used to meet the different requirements of different types of image content in the same image, thereby solving the problem of poor color filtering effect caused by using a single color filtering algorithm in complex scenes.

[0046] The following non-restrictive description of the image processing method provided according to at least one embodiment of the present disclosure is given through several examples or embodiments. As described below, different features in these specific examples or embodiments can be combined with each other without conflicting with each other to obtain new examples or embodiments, and these new examples or embodiments also fall within the scope of protection of the present disclosure.

[0047] Figure 1 A flowchart of an image processing method provided in at least one embodiment of the present disclosure.

[0048] At least one embodiment of the present disclosure provides an image processing method 10, such as Figure 1 For example, the image processing method 10 can be applied to any scene that requires image color filtering (or image color conversion), for example, it can be applied to ink screens, e-books, printers, etc., and can also be applied to other aspects, which are not limited by the embodiments of the present disclosure. Figure 1 As shown, the image processing method 10 may include the following steps S101 to S103.

[0049] Step S101: Acquire an input image.

[0050] Step S102: Based on the input image, obtain at least two intermediate images.

[0051] Step S103: Obtain an output image based on at least two intermediate images, where each of the at least two intermediate images corresponds to a single type of image content, different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes.

[0052] For example, in at least one embodiment of the present disclosure, in step S101, the input image can be any image to be processed. For example, the input image can be a color image or a grayscale image. For another example, the input image can be an advertising image, a badge image, a landscape image, etc. The embodiments of the present disclosure do not impose any restrictions on this, and can be set according to actual needs.

[0053] For example, in at least one embodiment of the present disclosure, for step S102, obtaining at least two intermediate images based on the input image may include: generating at least two intermediate images according to the type of image content in the input image. For example, the input image includes multiple types of image content, such as text, portraits, geometric figures (such as circles, rectangles, triangles, parallelograms, ellipses, semicircles), etc. For example, in one example, the input image is an advertising image, which may include a text introduction of the product, a face image of the spokesperson, and a geometric logo. For example, the intermediate image may be an image that only includes a single type of image content, such as a layer that only includes text, a layer that only includes portraits, a layer that only includes geometric figures, or a layer that only includes a background (such as the image background after removing text, portraits, and geometric figures). For example, in an embodiment of the present disclosure, the most appropriate or optimal color filtering algorithm may be selected and applied to each intermediate image to achieve the best color filtering effect. For example, in an embodiment of the present disclosure, a predetermined text color filtering algorithm is used for an intermediate image that only includes text, and a predetermined portrait color filtering algorithm is used for an intermediate image that only includes a portrait, etc. The embodiment of the present disclosure does not limit this.

[0054] It should be noted that, in the embodiments of the present disclosure, the “intermediate image” is not limited to a specific image or images, nor is it limited to a specific order, and can be set according to actual needs.

[0055] For example, in at least one embodiment of the present disclosure, for step S103, an output image is obtained based on at least two intermediate images. Each of the at least two intermediate images corresponds to a single type of image content, different intermediate images correspond to different image contents, and different intermediate images correspond to different color filtering processes. For example, in at least one embodiment of the present disclosure, at least two intermediate images are fused (merged) to obtain a final output image. In this way, the problem of poor color filtering effect caused by using a single color filtering algorithm in complex scenes can be solved. By generating multiple intermediate images from the input image and applying different color filtering processes to different intermediate images, different requirements of different image contents in the same image can be met, thereby improving the color filtering effect of the entire image.

[0056] Figure 2 An operational flowchart of an image processing method provided by at least one embodiment of the present disclosure.

[0057] For example, in at least one embodiment of the present disclosure, for step S102, based on the input image, obtaining at least two intermediate images may include the following operations S301 and S302, such as Figure 2 shown.

[0058] S301: Based on an input image, obtain at least two preliminary images, each of the at least two preliminary images corresponding to a single type of image content.

[0059] S302: Acquire at least two intermediate images based on the at least two preliminary images, where the at least two preliminary images correspond one-to-one to the at least two intermediate images.

[0060] For example, in at least one embodiment of the present disclosure, the preliminary image may refer to a mask image from an input image or a layer that has not been color filtered, and each preliminary image corresponds to a type of image content. It should be noted that in the embodiments of the present disclosure, the "preliminary image" is not limited to a specific image or images, nor is it limited to a specific order, and can be set according to actual needs. For example, in some examples, the mask image may be a binary image, for example, the pixel value of the target area or the area of interest is "255", and the pixel value of other areas is "0". Of course, the embodiments of the present disclosure are not limited to this. For example, in one example, the preliminary image may refer to a layer that only includes a single type of image content and has not been color filtered, and the embodiments of the present disclosure are not limited to this. In this way, different intermediate images with different color filtering processes applied can be obtained based on multiple preliminary images to obtain the final output image.

[0061] For example, in at least one embodiment of the present disclosure, the at least two preliminary images include at least two of a first preliminary image, a second preliminary image, a third preliminary image, and a fourth preliminary image. For example, in at least one embodiment of the present disclosure, the first preliminary image corresponds to text-type image content, the second preliminary image corresponds to portrait-type image content, the third preliminary image corresponds to geometric-type image content, and the fourth preliminary image corresponds to background-type image content. In this way, different preliminary images can be generated from the input image according to the type of image content, so that the optimal color filtering processing method can be selected for different types of image content.

[0062] It should be noted that in the embodiments of the present disclosure, the "first preliminary image", "second preliminary image", "third preliminary image" and "fourth preliminary image" are not limited to a specific image or images, nor are they limited to a specific order, and can be set according to actual needs.

[0063] It should also be noted that in the embodiments of the present disclosure, the input image may include one or more image types at the same time, and the first preliminary image, the second preliminary image, the third preliminary image and the fourth preliminary image are not necessarily obtained from the same input image at the same time, which depends on the actual situation.

[0064] Figure 3 A schematic diagram of an image processing solution provided in at least one embodiment of the present disclosure.

[0065] For example, in Figure 3 In the example shown, for the badge template image, a mask image mask2 (i.e., a first preliminary image) corresponding to the text type, a mask image mask3 (i.e., a second preliminary image) corresponding to the portrait type, and a mask image mask1 (i.e., a third preliminary image) corresponding to the geometric figure type can be obtained. For example, mask image mask2 can be considered a binary image with pixel values of "255" in the area corresponding to the text and pixel values of "0" in other areas. Mask image mask3 can be considered a binary image with pixel values of "255" in the area corresponding to the portrait or face and pixel values of "0" in other areas. Mask image mask1 can be considered a binary image with pixel values of "255" in the area corresponding to the geometric figure and pixel values of "0" in other areas. The embodiments of the present disclosure are not limited to this.

[0066] For example, in at least one embodiment of the present disclosure, in step S301, the operation of acquiring at least two preliminary images based on the input image may include: performing text detection on the input image to acquire a first preliminary image; performing portrait detection on the input image to acquire a second preliminary image; and / or performing geometric figure detection on the input image to acquire a third preliminary image. In this manner, multiple preliminary images can be automatically identified or segmented from the input image.

[0067] For example, in at least one embodiment of the present disclosure, various detection algorithms are used to detect and segment different types of image content in an image to be processed, i.e., an input image, before color filtering is performed. For example, detection algorithms may include text recognition algorithms, geometric shape detection algorithms, and portrait detection algorithms. The embodiments of the present disclosure do not impose specific limitations on this, and various conventional known detection algorithms may be used, as long as they can produce correct detection results.

[0068] For example, in Figure 3 In the example shown, a text recognition algorithm, a geometric shape detection algorithm, and a portrait detection algorithm are used to obtain a mask image mask2 (ie, a first preliminary image), a mask image mask3 (ie, a second preliminary image), and a mask image mask1 (ie, a third preliminary image).

[0069] Figure 4 A schematic diagram of a geometric figure detection algorithm provided for at least one embodiment of the present disclosure.

[0070] For example, in at least one embodiment of the present disclosure, Figure 4As shown, the geometric figure detection algorithm can detect simple geometric figures such as triangles, circles, rectangles, parallelograms, ellipses, semicircles, etc. from the input image, calculate the position and size of the geometric figures, and calculate the mask image of each geometric figure in the input image, that is, the third preliminary image.

[0071] Figure 5 A schematic diagram of a text detection algorithm provided for at least one embodiment of the present disclosure.

[0072] For example, in at least one embodiment of the present disclosure, Figure 5 As shown, the text detection algorithm consists of two steps: text recognition and text cutout. The text recognition algorithm detects whether the input image contains text and frames the text portion (text area) in the input image. The text cutout algorithm cuts out the text from the framed content to obtain a text mask, which is the first preliminary image.

[0073] Figure 6 A schematic diagram of a portrait detection algorithm provided in at least one embodiment of the present disclosure.

[0074] For example, in at least one embodiment of the present disclosure, Figure 6 As shown, the portrait detection algorithm consists of two steps: face recognition and portrait cutout. Face recognition detects whether an input image contains a portrait, such as an ID photo. The portrait cutout extracts the portrait detected by the face recognition algorithm, creating a mask representing the portrait, the second preliminary image.

[0075] For example, in at least one embodiment of the present disclosure, for step S301, the operation of obtaining at least two preliminary images based on the input image may include: obtaining a fourth preliminary image based on the first preliminary image, the second preliminary image, and the third preliminary image. For example, in at least one embodiment of the present disclosure, the fourth preliminary image corresponds to the remaining area after removing text, portraits, and geometric figures from the input image. For example, in one embodiment, the corresponding pixel values of the three mask images mask1, mask2, and mask3 mentioned above can be added and inverted to obtain a background mask image, that is, the fourth preliminary image. In this way, the background mask image, that is, the fourth preliminary image, can be obtained without adopting other recognition and detection methods.

[0076] For example, in at least one embodiment of the present disclosure, various color filtering algorithms can be used in a targeted manner according to the different types of image content, such as text color filtering algorithms, geometric figure color filtering algorithms, portrait color filtering algorithms, natural image color filtering algorithms (also referred to herein as background color filtering algorithms), etc. The embodiments of the present disclosure do not impose specific restrictions on the types of color filtering algorithms, and they can be set according to actual needs. For example, different color filtering algorithms can set different color filtering parameters to achieve the best color filtering effect for the target image. It should be noted that the various parameters of different color filtering algorithms can be pre-set, and the embodiments of the present disclosure do not impose any restrictions on this.

[0077] Figure 7 A schematic diagram of a text color filtering algorithm provided in at least one embodiment of the present disclosure.

[0078] For example, in at least one embodiment of the present disclosure, when filtering color for ordinary text, from the perspective of beauty and accuracy, the color filtering algorithm should prioritize the clarity of the text and the smoothness of the text lines, and use this as a starting point to set and adjust the algorithm parameters. Figure 7 The original text in the first line is processed by the general color filtering algorithm, and the color filtering effect of the text content is as follows Figure 7 As shown in the second row of . When the text image has problems such as compression and blurring, the color filtering result will have jagged edges and scattered points, which is not conducive to the clear display of the text content. In this case, by adjusting the parameters of the color filtering algorithm, a set of color filtering algorithms for ordinary text content is constructed to obtain clearer text content, such as Figure 7 The expected effect is shown in the third row.

[0079] Figure 8 A schematic diagram of a portrait color filtering algorithm provided in at least one embodiment of the present disclosure.

[0080] For example, in at least one embodiment of the present disclosure, the original portrait, the color filter effect using the general color filter algorithm, and the desired effect are as follows: Figure 8 As shown, when performing color filtering on a face image, the brightness and darkness of the skin color should be maintained, and the edge contour transition should be natural without a sense of boundary.

[0081] Figure 9 A schematic diagram of a geometric figure color filtering algorithm provided for at least one embodiment of the present disclosure.

[0082] For example, in at least one embodiment of the present disclosure, when adjusting the color filtering parameters of a geometric figure, the deformation and jagged edges of the figure should be avoided. At the same time, for the solid color filling inside the regular figure, it should be converted to a uniform color closest to the original color, or a compact distribution of pixels of several colors. For thinner lines, the continuity and smoothness of the converted lines should be maintained. Figure 9 shown.

[0083] Figure 10 A schematic diagram of a natural image color filtering algorithm provided in at least one embodiment of the present disclosure.

[0084] For example, in at least one embodiment of the present disclosure, for a natural image or background image, the sparseness and density of the pixels are used to present the intensity of the saturation, so as to present the grayscale and details of the original image as much as possible. Figure 10 shown.

[0085] For example, in at least one embodiment of the present disclosure, a corresponding intermediate image is obtained by applying a corresponding color filtering process based on the image type corresponding to a preliminary image. The plurality of intermediate images corresponds one-to-one to the plurality of preliminary images. For example, in at least one embodiment of the present disclosure, the at least two intermediate images include at least two of a first intermediate image, a second intermediate image, a third intermediate image, and a fourth intermediate image.

[0086] It should be noted that in the embodiments of the present disclosure, the "first intermediate image", "second intermediate image", "third intermediate image" and "fourth intermediate image" are not limited to a specific image or images, nor are they limited to a specific order, and can be set according to actual needs.

[0087] It should also be noted that in the embodiments of the present disclosure, one or more intermediate images can be obtained based on the input image. It is not necessary to obtain the first intermediate image, the second intermediate image, the third intermediate image and the fourth intermediate image at the same time based on the same input image, which depends on the actual situation.

[0088] For example, in at least one embodiment of the present disclosure, a corresponding color filtering algorithm may be determined based on the image content type corresponding to the preliminary image, thereby obtaining a corresponding intermediate image.

[0089] For example, in at least one embodiment of the present disclosure, for step S302, the operation of obtaining at least two intermediate images based on at least two preliminary images may include: obtaining a first intermediate image based on the first preliminary image and the input image after text color filtering; obtaining a second intermediate image based on the second preliminary image and the input image after portrait color filtering; obtaining a third intermediate image based on the third preliminary image and the input image after geometric color filtering; and / or obtaining a fourth intermediate image based on the fourth preliminary image and the input image after background color filtering. In this way, different intermediate images with different color filtering processes applied can be obtained, meeting the different requirements of different image contents within the same image and improving the color filtering effect of the entire image.

[0090] For example, in at least one embodiment of the present disclosure, text detection is used to obtain a mask image corresponding to the text area, text color filtering is performed on the input image, and the filtering result is superimposed on the mask image (for example, the input image after text color filtering is multiplied with the mask image) to obtain a first intermediate image. A similar method is used to obtain a second intermediate image, a third intermediate image, and a fourth intermediate image.

[0091] For example, in at least one embodiment of the present disclosure, obtaining an output image based on at least two intermediate images in step 102 may include merging the at least two intermediate images to obtain the output image. For example, in at least one embodiment of the present disclosure, merging the first, second, third, and fourth intermediate images to obtain a final, complete image, i.e., the output image. In this manner, merging different intermediate images that have undergone different color filtering processes to obtain the output image solves the problem of poor color filtering effects when using a single color filtering algorithm in complex scenes.

[0092] For example, in at least one embodiment of the present disclosure, combining the first, second, third, and fourth intermediate images to generate an output image may include: in response to the absence of overlap between the first, second, third, and fourth intermediate images, adding corresponding pixel values in the first, second, third, and fourth intermediate images to generate the output image. In this manner, when the intermediate images do not overlap, the output image can be generated by simple addition.

[0093] For example, in at least one embodiment of the present disclosure, merging the first, second, third, and fourth intermediate images to generate an output image may include: in response to an overlap between the first, second, third, and fourth intermediate images, basing pixel values of the overlapped portion on the highest-priority intermediate image among the at least two intermediate images that include the overlapped portion. In this manner, even when there is overlap between the intermediate images, the output image can be calculated by prioritizing the intermediate images.

[0094] Figure 11 A schematic block diagram of an image processing method provided by at least one embodiment of the present disclosure.

[0095] For example, in at least one embodiment of the present disclosure, Figure 11 As shown, the input image can be a picture selected by the user, for example, a picture in the format of jpg, png, bmp, etc. First, the geometric figure mask, text mask, and portrait mask in the input image are calculated through algorithms such as geometric figure detection, text detection, and portrait detection. The original input image is subjected to geometric figure color filtering, text color filtering, portrait color filtering, and background color filtering, respectively. The text color filtering result (i.e., color filtering result 1) is superimposed with the corresponding text mask, the portrait color filtering result (i.e., color filtering result 2) is superimposed with the corresponding portrait mask, the geometric figure color filtering result (i.e., color filtering result 3) is superimposed with the corresponding geometric figure mask, and the background color filtering result (i.e., color filtering result 4) is superimposed with the corresponding background mask, respectively. Four layers are obtained, namely, the filtered text (layer 1), the filtered portrait (layer 2), the filtered figure (layer 3), and the filtered background (layer 4). Finally, these four layers are merged to obtain a complete filtered image, i.e., the output image.

[0096] It should be noted that Figure 11 The specific implementation methods of the various block diagrams shown in can refer to the relevant description of the image processing method 10 and will not be repeated here.

[0097] For example, in at least one embodiment of the present disclosure, geometric figure detection is performed on the input image X. If a geometric figure is detected, a mask image M1 corresponding to the geometric figure is extracted, and the input image X is processed using a geometric figure color filtering algorithm to obtain image X1. Text detection is performed on the input image X. If text is detected, a mask image M2 corresponding to the text is extracted, and the input image X is processed using a text color filtering algorithm to obtain image X2. Portrait detection is performed on the input image X. If a portrait is detected, a mask image M3 corresponding to the portrait is extracted, and the input image X is processed using a portrait color filtering algorithm to obtain image X3. The input image X is processed using a background color filtering algorithm to obtain image X4. The mask images M1, M2, and M3 extracted in the above steps are added and inverted to obtain a mask image M4 corresponding to the background. For example, the final merged output image Y can be expressed as Y = X1×M1+X2×M2+X3×M3+X4×M4.

[0098] For example, in at least one embodiment of the present disclosure, if the first, second, third, and fourth intermediate images do not overlap, the merging process may be to add the corresponding pixel values of these four intermediate images. If there is an overlapping area between the first, second, third, and fourth intermediate images, the pixel values in the overlapping area may be determined by the priority of the intermediate image containing the overlapping area.

[0099] For example, in an embodiment of the present disclosure, the priority order of the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image can be set to be the first intermediate image>the second intermediate image>the third intermediate image>the fourth intermediate image. For example, when merging multiple intermediate images, if there is an overlapping area between different intermediate images, the pixel value of the overlapping area is determined based on the intermediate image with a higher priority. For example, in one example, for the overlapping area A, the intermediate image corresponding to the text (the first intermediate image) covers the overlapping area A, and the intermediate image corresponding to the geometric figure (the third intermediate image) also covers the overlapping area A. Then, when merging multiple intermediate images, the pixel value of the overlapping area A depends on the pixel value of the corresponding overlapping area A of the first intermediate image with a higher priority, rather than the pixel value of the corresponding overlapping area A of the third intermediate image.

[0100] It should be noted that the priority order of the at least two intermediate images can be set according to actual needs, and the embodiments of the present disclosure do not limit this.

[0101] For example, in at least one embodiment of the present disclosure, obtaining at least two preliminary images based on the input image in step S301 may include: in response to the input image being an editable vector image with at least two layers, using the at least two layers as the at least two preliminary images, each of the at least two layers corresponding to a single type of image content. If the input image is an editable vector image with at least two layers, there is no need to perform operations such as text detection, portrait detection, or geometric shape detection, and multiple preliminary images can be directly parsed and obtained.

[0102] Figure 12 A schematic block diagram of another image processing method provided by at least one embodiment of the present disclosure.

[0103] For example, in at least one embodiment of the present disclosure, for professional scenes, the input image can be a template image designed by a designer user. For example, an editable vector image PSD file output by professional software (such as Photoshop, etc.) can be directly used as the input for image processing, such as Figure 12 When designing a manuscript, designers can label each layer with a specific type, such as geometric shapes, text, portraits, or background images. By reading a PSD file and parsing the labeled names of each layer, at least two different types of layers, or preliminary images, can be obtained.

[0104] For example, in at least one embodiment of the present disclosure, for step S302, based on at least two preliminary images, obtaining at least two intermediate images includes: performing corresponding color filtering processing on each of the at least two preliminary images to obtain at least two intermediate images. Figure 12 As shown, different color filtering processes (e.g., geometric shape filtering, text filtering, portrait filtering, etc.) are applied to each of the parsed layers to obtain the corresponding intermediate image, i.e., the filtered layer. If the input image is an editable vector image with at least two layers, the corresponding color filtering process can be directly applied to each preliminary image to obtain the corresponding intermediate image.

[0105] For example, in at least one embodiment of the present disclosure, the filtered layers (i.e., the intermediate images) are merged to obtain a final PNG image. The layer merging operation can be referred to the description of step 103 in the image processing method 10 and will not be repeated here.

[0106] The image processing method 10 provided by at least one embodiment of the present disclosure meets the different requirements of different types of image content in the same image by applying different color filtering processing to at least two different types of intermediate images of the input image, thereby improving the color filtering effect of the entire image, thereby solving the problem of poor color filtering effect caused by using a single color filtering algorithm in complex scenes.

[0107] It should also be noted that, in various embodiments of the present disclosure, the order in which the steps of the image processing method 10 are executed is not limited. Although the execution process of each step is described above in a specific order, this does not constitute a limitation on the embodiments of the present disclosure. The steps in the image processing method 10 can be executed serially or in parallel, depending on actual needs. For example, the image processing method 10 can also include more or fewer steps, and the embodiments of the present disclosure are not limited in this regard.

[0108] At least one embodiment of the present disclosure also provides an image processing device, which can use different color filtering processing on at least two different types of intermediate images of an input image to meet the different requirements of different types of image content in the same image, improve the color filtering effect of the entire image, and thus solve the problem of poor color filtering effect caused by using a single color filtering algorithm in complex scenes.

[0109] Figure 13 A schematic block diagram of an image processing device provided in at least one embodiment of the present disclosure.

[0110] For example, in at least one embodiment of the present disclosure, Figure 13 As shown, the image processing device 40 includes an input module 401 , an acquisition module 402 and a processing module 403 .

[0111] For example, in at least one embodiment of the present disclosure, the input module 401 is configured to obtain an input image. For example, the input module 401 can implement step S101. The specific implementation method can refer to the relevant description of step S101 and will not be repeated here.

[0112] For example, in at least one embodiment of the present disclosure, the acquisition module 402 is configured to acquire at least two intermediate images based on the input image. For example, the acquisition module 402 may implement step S102. The specific implementation method thereof may refer to the relevant description of step S102 and will not be repeated here.

[0113] For example, in at least one embodiment of the present disclosure, processing module 403 is configured to obtain an output image based on at least two intermediate images, where each of the at least two intermediate images corresponds to a single type of image content, different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes. For example, processing module 403 may implement step S103. The specific implementation method thereof can be found in the description of step S103 and is not further described here.

[0114] It should be noted that these input modules 401, acquisition modules 402 and processing modules 403 can be implemented through software, hardware, firmware or any combination thereof. For example, they can be implemented as input circuit 401, acquisition circuit 402 and processing circuit 403 respectively. The embodiments of the present disclosure do not limit their specific implementation methods.

[0115] It should be understood that the image processing device 40 provided in the embodiment of the present disclosure can implement the aforementioned image processing method 10 and can also achieve technical effects similar to those of the aforementioned image processing method 10, which will not be elaborated here.

[0116] It should be noted that in the embodiments of the present disclosure, the image processing device 40 may include more or fewer circuits or units, and the connection relationship between the various circuits or units is not limited and can be determined according to actual needs. The specific configuration of each circuit is not limited and can be composed of analog devices, digital chips, or other applicable methods based on circuit principles.

[0117] Figure 14 The figure is a schematic block diagram of another image processing device provided by at least one embodiment of the present disclosure.

[0118] At least one embodiment of the present disclosure further provides an image processing device 90. Figure 14 As shown, the image processing apparatus 90 includes a processor 910 and a memory 920. The memory 920 includes one or more computer program modules 921. The one or more computer program modules 921 are stored in the memory 920 and are configured to be executed by the processor 910. The one or more computer program modules 921 include instructions for executing the image processing method 10 provided by at least one embodiment of the present disclosure. When executed by the processor 910, the one or more steps of the image processing method 10 provided by at least one embodiment of the present disclosure can be performed. The memory 920 and the processor 910 can be interconnected via a bus system and / or other form of connection mechanism (not shown).

[0119] For example, the processor 910 may be a central processing unit (CPU), a digital signal processor (DSP), or other processing units with data processing capabilities and / or program execution capabilities, such as a field programmable gate array (FPGA). For example, the central processing unit (CPU) may be an X86 or ARM architecture. The processor 910 may be a general-purpose processor or a dedicated processor, and may control other components in the image processing device 90 to perform desired functions.

[0120] For example, the memory 920 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, a flash memory, etc. One or more computer program modules 921 may be stored on the computer-readable storage medium, and the processor 910 may execute one or more computer program modules 921 to implement the various functions of the image processing device 90. The computer-readable storage medium may also store various applications and various data, as well as various data used and / or generated by the applications. The specific functions and technical effects of the image processing device 90 can be referred to the above description of the image processing method 10 and will not be repeated here.

[0121] Figure 15 A schematic block diagram of yet another image processing apparatus 600 provided in accordance with at least one embodiment of the present disclosure.

[0122] The terminal devices in the embodiments of the present disclosure may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 15 The image processing device 600 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0123] For example, Figure 15 As shown, in some examples, the image processing device 600 includes a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the computer system are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0124] For example, the following components can be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609 including, for example, a network interface card such as a LAN card, a modem, etc. The communication device 609 can allow the image processing device 600 to communicate with other devices wirelessly or by wire to exchange data, performing communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., are installed on the drive 610 as needed so that the computer program read therefrom can be installed into the storage device 608 as needed. Although Figure 15 The image processing apparatus 600 is shown as including various devices, but it should be understood that it is not required to implement or include all of the devices shown. More or fewer devices may be implemented or included instead.

[0125] For example, the image processing device 600 may further include a peripheral interface (not shown in the figure), etc. The peripheral interface may be various types of interfaces, such as a USB interface, a lightning interface, etc. The communication device 609 may communicate with a network and other devices through wireless communication, such as the Internet, an intranet, and / or a wireless network such as a cellular telephone network, a wireless local area network (LAN), and / or a metropolitan area network (MAN). Wireless communications may use any of a variety of communication standards, protocols, and technologies, including, but not limited to, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wi-Fi (e.g., based on IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n standards), Voice over Internet Protocol (VoIP), Wi-MAX, protocols for email, instant messaging, and / or Short Message Service (SMS), or any other suitable communication protocol.

[0126] For example, the image processing device 600 can be any device such as a mobile phone, tablet computer, laptop computer, e-book, game console, television, digital photo frame, navigator, etc., or it can be any combination of data processing devices and hardware. The embodiments of the present disclosure are not limited to this.

[0127] For example, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the image processing method 10 disclosed in the embodiment of the present disclosure is executed.

[0128] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0129] The computer-readable medium may be included in the image processing apparatus 300 , or may exist independently without being incorporated into the image processing apparatus 300 .

[0130] Figure 16 A schematic block diagram of a non-transitory readable storage medium provided for at least one embodiment of the present disclosure.

[0131] An embodiment of the present disclosure also provides a non-transitory readable storage medium. Figure 16 is a schematic block diagram of a non-transitory readable storage medium according to at least one embodiment of the present disclosure. Figure 16 As shown, the non-transitory readable storage medium 70 stores computer instructions 111 , which, when executed by a processor, perform one or more steps in the image processing method 10 described above.

[0132] For example, the non-transitory readable storage medium 70 can be any combination of one or more computer-readable storage media. For example, one computer-readable storage medium contains computer-readable program code for acquiring an input image, another computer-readable storage medium contains computer-readable program code for acquiring at least two intermediate images based on the input image, and yet another computer-readable storage medium contains computer-readable program code for acquiring an output image based on the at least two intermediate images, wherein each of the at least two intermediate images corresponds to a single type of image content, different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes. Of course, the aforementioned program codes can also be stored in the same computer-readable medium, and the embodiments of the present disclosure are not limited to this.

[0133] For example, when the program code is read by a computer, the computer can execute the program code stored in the computer storage medium and execute, for example, the image processing method 10 provided in any embodiment of the present disclosure.

[0134] For example, the storage medium may include a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a flash memory, or any combination of the above storage media, or other suitable storage media. For example, the readable storage medium may also be Figure 14 For the memory 920 in the embodiment, the related description can be referred to the aforementioned content and will not be repeated here.

[0135] An embodiment of the present disclosure also provides an electronic device. Figure 17 is a schematic block diagram of an electronic device according to at least one embodiment of the present disclosure. Figure 17 As shown, the electronic device 120 may include the image processing apparatus 40 / 90 / 600 as described above. For example, the electronic device 120 may implement the image processing method 10 provided by any embodiment of the present disclosure.

[0136] In the present disclosure, the term "plurality" refers to two or more than two, unless clearly defined otherwise.

[0137] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0138] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An image processing method, comprising: Get the input image; Based on the input image, acquiring at least two intermediate images; acquiring an output image based on the at least two intermediate images; wherein each of the at least two intermediate images corresponds to a single type of image content, different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes; Wherein, acquiring the at least two intermediate images based on the input image comprises: acquiring at least two preliminary images based on the input image, wherein each of the at least two preliminary images corresponds to a single type of image content; acquiring the at least two intermediate images based on the at least two preliminary images, wherein the at least two preliminary images correspond one-to-one to the at least two intermediate images; Wherein, based on the input image, obtaining at least two preliminary images includes: in response to the input image being an editable vector image with at least two layers, using the at least two layers as the at least two preliminary images, wherein each of the at least two layers corresponds to a single type of image content; Wherein, acquiring the at least two intermediate images based on the at least two preliminary images includes: performing corresponding color filtering processing on each of the at least two preliminary images to obtain the at least two intermediate images.

2. The method according to claim 1, wherein The at least two preliminary images include at least two of a first preliminary image, a second preliminary image, a third preliminary image, and a fourth preliminary image; The first preliminary image corresponds to text-type image content, the second preliminary image corresponds to portrait-type image content, the third preliminary image corresponds to geometric-type image content, and the fourth preliminary image corresponds to background-type image content.

3. The method according to claim 2, wherein: Based on the input image, acquiring the at least two preliminary images comprises: Performing text detection on the input image to obtain the first preliminary image; Performing portrait detection on the input image to obtain the second preliminary image; and / or Perform geometric figure detection on the input image to obtain the third preliminary image.

4. The method according to claim 2, wherein: Based on the input image, acquiring the at least two preliminary images comprises: The fourth preliminary image is acquired based on the first preliminary image, the second preliminary image, and the third preliminary image.

5. The method according to claim 2, wherein: Acquiring the at least two intermediate images based on the at least two preliminary images comprises: obtaining a first intermediate image based on the first preliminary image and the input image after the text color filter processing is applied; obtaining a second intermediate image based on the second preliminary image and the input image after the portrait color filter processing is applied; Obtaining a third intermediate image based on the third preliminary image and the input image after applying geometric filtering; and / or A fourth intermediate image is obtained based on the fourth preliminary image and the input image after background color filtering.

6. The method according to claim 5, wherein: Acquiring the output image based on the at least two intermediate images includes: The first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image are combined to obtain the output image.

7. The method according to claim 6, wherein: Merging the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image to obtain the output image includes: In response to the fact that there is no overlapping part among the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image, corresponding pixel values in the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image are added to obtain the output image.

8. The method according to claim 6, wherein: Merging the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image to obtain the output image includes: In response to an overlapping portion among the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image, a pixel value of the overlapping portion is based on an intermediate image with a highest priority among at least two intermediate images including the overlapping portion.

9. The method according to claim 5, wherein: The priority order of the first intermediate image, the second intermediate image, the third intermediate image, and the fourth intermediate image is the first intermediate image>the second intermediate image>the third intermediate image>the fourth intermediate image.

10. The method according to claim 1, wherein Each of the at least two preliminary images is a binary image.

11. The method according to claim 1, wherein The different color filtering processes at least include using different color filtering parameters.

12. An image processing device, comprising: An input module configured to obtain an input image; an acquisition module, configured to acquire at least two intermediate images based on the input image; a processing module configured to obtain an output image based on the at least two intermediate images; wherein each of the at least two intermediate images corresponds to a single type of image content, different intermediate images correspond to different types of image content, and different intermediate images correspond to different color filtering processes; Wherein, acquiring the at least two intermediate images based on the input image comprises: acquiring at least two preliminary images based on the input image, wherein each of the at least two preliminary images corresponds to a single type of image content; acquiring the at least two intermediate images based on the at least two preliminary images, wherein the at least two preliminary images correspond one-to-one to the at least two intermediate images; Wherein, based on the input image, obtaining at least two preliminary images includes: in response to the input image being an editable vector image with at least two layers, using the at least two layers as the at least two preliminary images, wherein each of the at least two layers corresponds to a single type of image content; Wherein, acquiring the at least two intermediate images based on the at least two preliminary images includes: performing corresponding color filtering processing on each of the at least two preliminary images to obtain the at least two intermediate images.

13. An image processing apparatus, comprising: processor; a memory comprising one or more computer program modules; The one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for executing the image processing method according to any one of claims 1 to 11.

14. A non-transitory readable storage medium having computer instructions stored thereon, wherein: When the computer instructions are executed by a processor, the image processing method according to any one of claims 1 to 11 is performed.

Images