CN111160359A - Digital image processing method - Google Patents
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- CN111160359A CN111160359A CN201911341099.0A CN201911341099A CN111160359A CN 111160359 A CN111160359 A CN 111160359A CN 201911341099 A CN201911341099 A CN 201911341099A CN 111160359 A CN111160359 A CN 111160359A
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Abstract
The invention provides a digital image processing method, which comprises the following steps: acquiring a target image; acquiring a plurality of feature points in a target image, performing feature point classification identification on each feature point, and generating a plurality of same images based on feature point categories; classifying according to different feature points, and respectively carrying out various image preprocessing on classified images to form a plurality of images to be corrected of different types; carrying out image correction on the image to be corrected according to the image preprocessing classification and the feature point classification to generate a corrected image; the method can optimize the processing of the image or the video image so as to overcome the defects of excessive memory resources occupied and low processing efficiency when the image and/or the video image is processed in the prior art, and can quickly process the color of the image.
Description
Technical Field
The invention relates to the field of image processing, in particular to a digital image processing method.
Background
Digital Image Processing (also called computer Image Processing) refers to a process of converting an Image signal into a Digital signal and Processing the Digital signal by a computer. Digital image processing was first in the 50's of the 20 th century, and electronic computers were developed to some extent, and people began to use computers to process graphic and image information. Digital image processing was formed as a discipline in the early 60's of the 20 th century approximately. The purpose of early image processing was to improve the quality of images, which were targeted at human subjects with the goal of improving human visual effects. In image processing, an image with low quality is input, an image with improved quality is output, and common image processing methods include image enhancement, restoration, encoding, compression, and the like.
Image processing is the act of processing image information to meet the visual and psychological needs of a person or the application needs. Vision is the most prominent means for humans to obtain information from nature. Statistically, the visual information accounts for about 60%, the auditory information accounts for about 20%, and other information such as taste information, tactile information, etc. accounts for about 20% of the information obtained by human beings. The importance of visual information to humans is thus visible, while images are just the main routes for human capture of visual information. The so-called "map" is the distribution of light transmitted or reflected by an object; the "image" is an impression or recognition in the brain that the human visual system receives information of the map to form. The former is objectively present, while the latter is human perception, and the image is a combination of both.
With the development of scientific technology and the improvement of the living standard of people, the appearance of digital cameras and the development of digital image processing technology, people are more and more attracted wide attention in the new and advanced digital age, and digital image processing becomes necessary basic knowledge. Image processing techniques have also seen unprecedented growth and adoption for recent decades due to the explosive growth of computer technology. At present, image processing technology has been widely applied to the fields of industry, military, medicine, traffic, agriculture, weather forecast, monitoring and alarm systems of banks, supermarkets and important departments, video telephones, network transmission and the like, and becomes the subject of study and research of various disciplines.
Disclosure of Invention
The embodiment of the invention is realized by the following steps:
a digital image processing method, characterized by comprising the steps of:
acquiring a target image;
acquiring a plurality of feature points in a target image, performing feature point classification identification on each feature point, and generating a plurality of same images based on feature point categories;
classifying according to different feature points, and respectively carrying out various image preprocessing on classified images to form a plurality of images to be corrected of different types;
carrying out image correction on the image to be corrected according to the image preprocessing classification and the feature point classification to generate a corrected image;
and synthesizing a plurality of corrected images to restore the images, and outputting the processed images.
In some embodiments of the present invention, the method for obtaining feature points includes converting a target image into a plurality of recognizable regions according to image attributes and image contents by using a trained neural network, dividing the image contents in the recognizable regions according to pixel points, setting coordinate values of the pixel points according to positions of the pixel points, and quantizing the image according to the pixel points to form a plurality of identical images with different feature point categories.
In some embodiments of the invention, the identification area comprises at least 1 identifiable object, and the object has an identifiable outline, the classification of the portion is determined by an outline feature having a specified characteristic in the target image, and the specific feature in the image is enhanced by an enhancement algorithm.
In some embodiments of the present invention, the method according to pixel quantization comprises:
establishing a gradient image from the digital image by assigning a value to each pixel;
distributing a neighborhood of a corresponding pixel for each pixel, applying a filter to obtain a corresponding value of the pixel, and filtering the gradient image to obtain a filtered image;
and establishing a position mask of the characteristic identification area corresponding to each pixel point in the corresponding identification area, and determining a position mask value for the difference between the pixel value in the filtered image and the pixel value in the gradient image in the mask.
In some embodiments of the invention, the pretreatment method comprises:
color image processing in which a color system is introduced so that it can process various color images;
morphological processing, namely, extracting specific features of various forms in the image according to different feature classifications;
dividing the image, namely splitting each element forming the expression content of the original image into mutually independent and complete whole bodies according to the expression content of the image;
compression encoding, which compresses and encodes the storage size of an image and reduces the amount of data used when storing the image by using the redundancy of the image itself.
In some embodiments of the present invention, the method further includes performing noise reduction processing on the input image, generating a noise-reduced image, and performing noise reduction processing on the pixel matrix of the target image by using a convolutional neural network, including adjusting brightness of light, contrast, spatial resolution, and gray scale resolution of the input target image.
In some embodiments of the invention, the image correction includes geometric correction of the image and gray scale correction of the image,
the geometric correction of the image comprises the steps of carrying out space coordinate transformation on the image, establishing a mapping relation between the coordinates of pixel points of the image and the coordinates of corresponding points of a target image, and then correcting the coordinates of each pixel of the image according to the mapping relation;
the gray level correction comprises the steps of enabling half of the image to be dark and half of the image to be bright aiming at image imaging unevenness, and carrying out gray level correction on the image in different degrees point by point so as to enable the gray level of the whole image to be uniform;
a gray scale transform is used for underexposure of a certain portion or the whole image.
In some embodiments of the present invention, the image restoration method is to establish a degradation model according to the imaging system, and apply an inverse process of the degradation process to restore the original image.
The embodiment of the invention at least has the following advantages or beneficial effects:
the image or video image processing method can be used for processing the image or video image in an optimized mode, so that the defects that in the prior art, the image and/or video image processing method occupies too much memory resources and is low in processing efficiency are overcome, and the color processing of the image can be performed quickly.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a digital image processing method according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the embodiments of the present invention, it should be noted that, if the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are usually placed in when used, the orientations or positional relationships are only used for convenience of describing the present invention and simplifying the description, but the terms do not indicate or imply that the devices or elements indicated must have specific orientations, be constructed in specific orientations, and operate, and therefore, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not require that the components be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, "a plurality" represents at least 2.
In the description of the embodiments of the present invention, it should be further noted that unless otherwise explicitly stated or limited, the terms "disposed," "mounted," "connected," and "connected" should be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
Example 1
A digital image processing method, as shown in fig. 1, comprising the steps of:
acquiring a target image;
acquiring a plurality of feature points in a target image, performing feature point classification identification on each feature point, and generating a plurality of same images based on feature point categories;
classifying according to different feature points, and respectively carrying out various image preprocessing on classified images to form a plurality of images to be corrected of different types;
carrying out image correction on the image to be corrected according to the image preprocessing classification and the feature point classification to generate a corrected image;
and synthesizing a plurality of corrected images to restore the images, and outputting the processed images.
In some embodiments of the present invention, the method for obtaining feature points includes converting a target image into a plurality of recognizable regions according to image attributes and image contents by using a trained neural network, dividing the image contents in the recognizable regions according to pixel points, setting coordinate values of the pixel points according to positions of the pixel points, and quantizing the image according to the pixel points to form a plurality of identical images with different feature point categories.
In some embodiments of the invention, the identification area comprises at least 1 identifiable object, and the object has an identifiable outline, the classification of the portion is determined by an outline feature having a specified characteristic in the target image, and the specific feature in the image is enhanced by an enhancement algorithm.
In some embodiments of the present invention, the method according to pixel quantization comprises:
establishing a gradient image from the digital image by assigning a value to each pixel;
distributing a neighborhood of a corresponding pixel for each pixel, applying a filter to obtain a corresponding value of the pixel, and filtering the gradient image to obtain a filtered image;
and establishing a position mask of the characteristic identification area corresponding to each pixel point in the corresponding identification area, and determining a position mask value for the difference between the pixel value in the filtered image and the pixel value in the gradient image in the mask.
In some embodiments of the invention, the pretreatment method comprises:
color image processing in which a color system is introduced so that it can process various color images;
morphological processing, namely, extracting specific features of various forms in the image according to different feature classifications;
dividing the image, namely splitting each element forming the expression content of the original image into mutually independent and complete whole bodies according to the expression content of the image;
compression encoding, which compresses and encodes the storage size of an image and reduces the amount of data used when storing the image by using the redundancy of the image itself.
In some embodiments of the present invention, the method further includes performing noise reduction processing on the input image, generating a noise-reduced image, and performing noise reduction processing on the pixel matrix of the target image by using a convolutional neural network, including adjusting brightness of light, contrast, spatial resolution, and gray scale resolution of the input target image.
In some embodiments of the invention, the image correction includes geometric correction of the image and gray scale correction of the image,
the geometric correction of the image comprises the steps of carrying out space coordinate transformation on the image, establishing a mapping relation between the coordinates of pixel points of the image and the coordinates of corresponding points of a target image, and then correcting the coordinates of each pixel of the image according to the mapping relation;
the gray level correction comprises the steps of enabling half of the image to be dark and half of the image to be bright aiming at image imaging unevenness, and carrying out gray level correction on the image in different degrees point by point so as to enable the gray level of the whole image to be uniform;
a gray scale transform is used for underexposure of a certain portion or the whole image.
In some embodiments of the present invention, the image restoration method is to establish a degradation model according to the imaging system, and apply an inverse process of the degradation process to restore the original image.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A digital image processing method, characterized by comprising the steps of:
acquiring a target image;
acquiring a plurality of feature points in a target image, performing feature point classification identification on each feature point, and generating a plurality of same images based on feature point categories;
classifying according to different feature points, and respectively carrying out various image preprocessing on classified images to form a plurality of images to be corrected of different types;
carrying out image correction on the image to be corrected according to the image preprocessing classification and the feature point classification to generate a corrected image;
and synthesizing a plurality of corrected images to restore the images, and outputting the processed images.
2. The digital image processing method according to claim 1, wherein the method for obtaining the feature points comprises the steps of converting a target image into a plurality of recognizable regions according to image attributes and image contents by using a trained neural network, dividing the image contents in the recognizable regions according to pixel points, setting coordinate values of the pixel points according to the positions of the pixel points, and quantizing the image according to the pixel points to form a plurality of identical images with different feature point categories.
3. The digital image processing method of claim 2, wherein at least 1 recognizable object is included in the recognition area, and the object has a recognizable outline, the classification of the portion is determined by an outline feature having a specified characteristic in the target image, and the specific feature in the image is enhanced by an enhancement algorithm.
4. The digital image processing method according to claim 2, wherein the method according to pixel quantization is:
establishing a gradient image from the digital image by assigning a value to each pixel;
distributing a neighborhood of a corresponding pixel for each pixel, applying a filter to obtain a corresponding value of the pixel, and filtering the gradient image to obtain a filtered image;
and establishing a position mask of the characteristic identification area corresponding to each pixel point in the corresponding identification area, and determining a position mask value for the difference between the pixel value in the filtered image and the pixel value in the gradient image in the mask.
5. The digital image processing method of claim 1, wherein the preprocessing method comprises:
color image processing in which a color system is introduced so that it can process various color images;
morphological processing, namely, extracting specific features of various forms in the image according to different feature classifications;
dividing the image, namely splitting each element forming the expression content of the original image into mutually independent and complete whole bodies according to the expression content of the image;
compression encoding, which compresses and encodes the storage size of an image and reduces the amount of data used when storing the image by using the redundancy of the image itself.
6. The method according to claim 1, further comprising performing noise reduction processing on the input image to generate a noise-reduced image, and performing noise reduction processing on the pixel matrix of the target image by using a convolutional neural network, wherein the noise reduction processing includes adjustment of light brightness, contrast adjustment, spatial resolution adjustment and gray scale resolution adjustment of the input target image.
7. The digital image processing method of claim 1, wherein the image correction includes geometric correction of the image and gradation correction of the image,
the geometric correction of the image comprises the steps of carrying out space coordinate transformation on the image, establishing a mapping relation between the coordinates of pixel points of the image and the coordinates of corresponding points of a target image, and then correcting the coordinates of each pixel of the image according to the mapping relation;
the gray level correction comprises the steps of enabling half of the image to be dark and half of the image to be bright aiming at image imaging unevenness, and carrying out gray level correction on the image in different degrees point by point so as to enable the gray level of the whole image to be uniform;
a gray scale transform is used for underexposure of a certain portion or the whole image.
8. The method of claim 1, wherein the image restoration is performed by building a degradation model according to the imaging system and applying an inverse process of the degradation process to restore the original image.
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CN114187865A (en) * | 2021-11-03 | 2022-03-15 | 北京易美新创科技有限公司 | Image processing method and device for LED display screen and control card |
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JP2010122993A (en) * | 2008-11-20 | 2010-06-03 | Panasonic Electric Works Co Ltd | Face authentication system |
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