Image deconvolution method based on total variation regularization

Abstract

The invention discloses an image deconvolution method based on total variation regularization. The image deconvolution method comprises the following steps: firstly, carrying out deconvolution on an image by using a TV (Total Variation) regularized Richardson-Lucy (RL) deconvolution method, so as to obtain a clear image serving as a reference image ug; secondly, carrying out edge detection on the reference image by using a canny operator, so as to obtain an image flat region and a grain region; thirdly, adopting a standard Richardson-Lucy algorithm in the grain region and a applying a TV regularized Richardson-Lucy algorithm in the flat region so as to obtain an image u<f> clearer than the reference image; and at last, carrying out bilateral filtering on the obtained image to obtain a detail layer u<d> of a newly obtained image u and finally adding the clear image u<f> with the detail layer u<d>, so as to obtain a clear image u. Shown by a simulation result, the image deconvolution method based on the total variation regularization is better than the standard RL algorithm and the TV regularized RL algorithm in ringing effect restraining and detail preserving.

Description

technical field [0001] The invention relates to image processing, in particular to an image deconvolution method based on total variation regular constraints. Background technique [0002] With the rapid development of modern imaging technology, the image data generated are unprecedented in detail and volume, and the processing of these image data makes image processing and computer vision more and more important. Image restoration is an important part of image processing. Its main purpose is to reduce or eliminate the quality degradation in the process of image acquisition or transmission, so that the obtained image is as close as possible to the ideal clear image, improve the visual effect of the image, and restore the image. various information in . Since the acquired images cannot be reproduced in many scenes, such as images acquired by video surveillance, astronomical images captured at a certain moment, reconnaissance photos, etc., if there are important text and othe...

AI Technical Summary

Problems solved by technology

Although these algorithms can better suppress the ringing effect and reduce noise, the resulting image will be too smooth and some image details will be lost.

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