Image contrast enhancement implementation method based on local adaptive gamma correction

Abstract

The invention discloses an image contrast enhancement implementation method based on local adaptive gamma correction, and solves the problems of overexposure, underexposure and backlight of an image.The method comprises the steps of inputting a to-be-processed image, expanding the to-be-processed image and transferring the expanded image to an HSV space; channel separation; partitioning the V-channel image; calculating to obtain an initial gray scale transformation function and carrying out interpolation mapping on the V-channel image by using the initial gray scale transformation function; histogram clipping and shift compensation are carried out; calculating a final gray scale transformation function and carrying out interpolation mapping on the Vg channel image; and merging and converting the channels an RGB image. According to the invention, targeted local adaptive gamma correction is carried out on a problem image; according to the method, the contrast ratio is effectively improved with high quality under the condition that the image is not distorted, the recovery effect of the texture and details of the image is remarkable, the complexity is low, the engineering applicability is high, and the method can be widely applied to image enhancement processing of image overexposure, overexposure and backlight caused by shooting scenes and techniques.

Description

technical field [0001] The present invention belongs to the technical field of image enhancement, and further relates to an image contrast enhancement method, specifically an image contrast enhancement implementation method based on local adaptive gamma (Gamma) correction, which can be used when the shooting environment is too bright or too dark The captured images show overexposure, underexposure or backlighting problems, enhance the contrast of such images and restore the texture details of such images. Background technique [0002] The main media for humans to transmit information is voice and images, of which visual information accounts for 80% of the various information received by humans. With the development of science and technology, the application of images has become more and more extensive, and has penetrated into many fields including communications, industry, Medical care, aerospace and other aspects, so, as an important medium for transmitting information, ima...

AI Technical Summary

Problems solved by technology

Traditional histogram equalization can play a good role in enhancing contrast, but it lacks the consideration of local images. Directly performing global histogram equalization directly leads to the inversion of local brightness of the image in some cases, and the color is distorted. At the same time Global histogram equalization has weak ability to restore image details

Contrast-limited adaptive histogram equalization On the basis of traditional histogram equalization, the idea of ​​​​blocking is added, and at the same time, histogram clipping is used to limit the excessive improvement of contrast. This method enhances the contrast of the image more naturally than global histogram equalization , the detail recovery is more obvious, but the noise of some scene images is over-amplified, and the degree of texture detail recovery still needs to be strengthened

[0006] Both gamma transformation and histogram enhancement in grayscale enhancement can enhance the contrast of the image, but the focus of the two is different. At the same time, the manual parameter adjustment of gamma transformation is very inefficient and its engineering applicability is not high with manual participation. However, the histogram enhancement is easy to amplify the noise and restore the details of the image is not obvious enough.

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