Image deconvolution method based on super laplace apriori constraint

Abstract

The invention discloses an image deconvolution method based on super laplace apriori constraint. The image deconvilution method comprises the following steps: firstly carrying out deconvolution on an image using a super laplace hypothesis apriori constraint deconvolution method to obtain a vivid image as a reference image Ig; secondly carrying out edge detection on the reference image utilizing a canny operator, and performing morphological dilation on the obtained image to obtain an image flat region and an image texture region; thirdly applying the standard a Richardson-Lucy algorithm to the texture region and applying the Richardson-Lucy algorithm of super laplace apriori regular constraint to the flat region to obtain an image If more vivid than the reference image; and finally carrying out bilateral filtering on the image If to obtain an image detail layer Id which is finally added to the vivid image If to obtain a vivid image I. With the adoption of the image deconvolution method, the ringing effect and further noise amplification can be restrained through the combination of the standard Richardson-Lucy algorithm and the super laplace apriori deconvolution method, and meanwhile much more image detailed information is kept.

Description

technical field [0001] The invention relates to image processing, in particular to an image deconvolution method based on super-Laplace prior constraints. Background technique [0002] With the development of modern digital technology and the popularization of image imaging equipment, image acquisition tools such as digital cameras are becoming more and more common in human daily life. However, during the acquisition process of digital images, camera shake during the long exposure time of acquisition devices such as digital cameras often makes the obtained images blurred. For image acquisition, many scenes, such as traffic monitoring, only happen instantaneously and cannot be reproduced. If there are important text and other identification information in the captured image record, it may be unrecognizable due to the blurred image. Therefore, how to restore blurred images is becoming more and more important. [0003] Usually, we express the blurred image as the convolution ...

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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