Image defuzzification method based ontotal variation of reweightedgraph

Abstract

The invention provides an image defuzzification method based on thetotal variation of a reweightedgraph. Based on graph-basedimage prior, spectral analysis and image defuzzification algorithm, the method mainly comprises the steps of firstly, adopting a refined image as an agent; secondly, designing a reweightedgraphtotal variation (RGTV) priori to effectively promote the double-mode edge weight distribution of a given fuzzy block; thirdly, adopting a new weight formula to carry out spectral analysis on the RGTV in the chart frequency domain, and obtaining a desired performance of the RGTV prior; furthermore, designing a high-efficiency algorithm through the spectral analysis of the RGTV toalternately carry out operation on the refined image and the fuzzy core; finally, restoringa target image through an operation fuzzy core and a nearest non-fuzzy image defuzzification algorithm. Compared with a current de-fuzzing method, the method can be used for removing morefuzzy imagetypes. The obtained result is more perfect in visual effect and numerical value expression.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image deblurring method based on total variation of reweighted graphs. Background technique [0002] In the process of image acquisition, transmission and storage, due to various factors, such as the turbulence effect of the atmosphere, the diffraction of the optical system in the camera equipment, the nonlinearity of the sensor characteristics, the aberration of the optical system, the relative distance between the imaging equipment and the object, etc. Motion, etc. will cause image blur problems, which will seriously affect the visual effect of the image, so it is especially important to perform deblurring operations on the image. In video diagnosis technology, image deblurring can diagnose low-quality video such as blurring, noise, abnormal brightness, and video loss in video images and common camera failures, effectively preventing problems caused by low image quality cause...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems that the existing deblurring methods can deal with limited blur forms, and are not ideal in terms of visual effects and numerical representations, the purpose of the present invention is to provide an image deblurring method based on the total variation of the weighted image. refine the image as a proxy; then, design a Reweighted Graph Total Variation (RGTV) prior to efficiently promote the distribution of bimodal edge weights for a given blurred patch; next, by adopting a new weighting formula for RGTV in Perform spectral analysis in the frequency domain of the graph, so that RGTV can obtain the desired performance a priori; further, use the spectral analysis of RGTV to design an efficient algorithm to alternately operate on the refined image and the blur kernel; finally, by computing the blur kernel and the nearest The unblurred image deblurring algorithm to restore the target image

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