Image restoration method based on double-edge wave filter and margin deconvolution

Abstract

The invention discloses an image restoration method based on a double-edge wave filter and margin deconvolution, comprising the following steps: (1) obtaining a restoration image with rings by adopting a standard RL algorithm to carry out higher iteration according to an image to be restored and a known fuzzy core; (2) adopting a double-edge wave filter to carry out filtering operation of the restoration image with rings to obtain the restoration image without rings; (3) carrying out the deconvolution on the restoration image without rings by the known fuzzy core to obtain a re-fuzzy image; (4) obtaining a restoration image detail by adopting the margin deconvolution by the re-fuzzy image obtained by the step (3) and the image to be restored, and adding the restoration image detail with the restoration image after eliminating ring to obtain a new restoration image containing ring; and (5) repeating the steps (2)-(4) for many times to obtain a final restoration image. The image restoration method can effectively inhibit middle and high frequency rings in the restoration image of the iterative algorithm, remain rich image details and obtain high-quality restoration images.

Description

technical field [0001] The invention relates to computer image processing technology, in particular to an image deblurring method. Background technique [0002] In the process of taking pictures, due to the hand shake of the photographer or the high-speed movement of the photographed object, the image is blurred. In order to avoid blurring, the usual method is to shorten the exposure time or use a tripod to shoot. However, the former will produce serious blurring under low light conditions. Noise, the latter is too bulky, so how to use software to restore blurred images is particularly important, which is a hot spot in the field of modern image processing. The inverse process of image blurring is called image deconvolution. Its purpose is to reversely obtain a clear image from a blurred image. It is of great significance in the fields of astronomical photography and medical imaging. [0003] Image deconvolution originated from astronomical observations. Earlier algorithms i...

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Problems solved by technology

[0003] Image deconvolution originated from astronomical observations. Earlier algorithms included standard RL algorithm, Wiener filter, etc. Later, overall variational regularization algorithm and image restoration algorithm in wavelet domain appeared. However, even if the blur kernel is known, due to The existence of the Gibbs phenomenon will lead to the ringing effect and affect the restoration effect. For example, the RL algorithm gradually restores the image details with the increase of the number of iterations, and the ringing effect will become more and more obvious. Although the overall variational regularization algorithm has a certain degree of The ringing is suppressed, but the image details are lost, so how to suppress the ringing while maintaining the image details is the key to image restoration

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