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Adaptive colour image processing method and system
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A color image and processing method technology, applied in the field of image processing, can solve the problems of reduced contrast in bright areas of images and over-enhancement at the edges of images, etc., and achieves a good enhancement effect.
Inactive Publication Date: 2011-01-19
BEIJING UNIV OF TECH
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[0004] In view of this, the present invention provides an adaptive color image processing method and system, which solves the problem of lower contrast in bright areas of the image, over-enhancement at the edge of the image, and Issues with halo and graying effects along image edges
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[0057] Such as figure 1 Shown, method of the present invention mainly comprises the following steps:
[0058] Step S101, read a frame of image to be processed.
[0059] Execute step S101, read a frame of video image I(x)=(I R (x), I G (x), I B (x)) T , the video image is the image to be processed, which is a color image with three components of R, G, and B. Among them, the image to be processed is W pixels wide and H pixels high. Since the image is a two-dimensional space, x in the above formula represents a vector, and x can also be represented by (m, n), where 0≤m≤ W-1, 0≤n≤H-1, m, n, W, H are all non-negative integers. Step S102, using the nonlinear inverse S-type function to perform global adaptive transformation on the luminance component of the image to be processed to obtain a globally enhanced luminance image L a (x).
[0092] On the basis of the above disclosed embodiments of the present invention, the present invention also discloses an adaptive color image processing method, please refer to the attached image 3 , mainly including the following steps:
[0093] Step S101, read a frame of image to be processed.
[0094] Step S1021, determine the interval range of the nonlinear mapping, map the gray scale range of the luminance component L(x) into the interval range, and obtain a gray scale compressed image L'(x).
[0095] In the sigmoid function, dynamically determine the grayscale mapping interval [v min , v max ]. v min The value of is represented by the following linear piecewise function.
[0096] v min = - 6 , M ≤ 0.3 0.04 ...
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Abstract
The invention discloses an adaptive colour imageprocessing method and an adaptive colour imageprocessingsystem. The method comprises the following steps of: reading an image to be processed; carrying out overall adaptive transformation on the luminance component of the image to be processed through a non-linear inverse S-type function to obtain an overall luminance enhancement image; regulating the local contrast ratio of the overall luminance enhancement image according to the corresponding relation between the central pixel luminance and the neighborhood pixel luminance of the overall luminance enhancement image to obtain a local luminance enhancement image; and carrying out linear colour restoration on the image to be processed according to the ratio between the luminance components of the local luminance enhancement image and the image to be processed to obtain a colour enhancement image. By the method and through the system of the invention, the luminance of the image to be processed can be adaptively regulated; the contrast ratio of a back area and a bright area is enhanced at the same time; the local contrast ratio of the image to be processed can be effectively enhanced; the over-enhancement phenomenon generated at the edge of the image is inhibited; the colour of the image is well kept; and better enhancement effect is achieved.
Description
technical field [0001] The present invention relates to the field of image processing, and more specifically, relates to an adaptive color imageprocessing method and system. Background technique [0002] At present, in most cases, in the process of acquiring color images, due to the limited dynamic range of the imaging sensor, the acquired images are partially underexposed, dark in color, low in contrast and loss of details, which greatly limits and affects subsequent image analysis and analysis. understanding of the research work. Therefore, it is necessary to enhance the collected image to improve the visual effect of the image and highlight the features of the image. Common grayscaleimage enhancement techniques in the prior art include global enhancement algorithms such as gamma correction, autolevels, USM sharpening (UnSharp Masking, USM), histogramequalization, and local histogramequalization, simultaneous Local enhancement algorithms such as state filtering. How...
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