Image restoration method in combination with weight factor and gradient restriction

Abstract

The invention discloses an image restoration method in combination with a weight factor and gradient restriction. The image restoration method comprises the steps of (1) mathematically deducing and establishing an image restoration model by uniting a Bayes frame and image relevant characteristics, (2) designing a weight factor coefficient matrix, and (3) optimizing and obtaining the optimal restored image. The image restoration method in combination with the weight factor and the gradient restriction is characterized in that the requirements of signal fidelity and detail retention in image restoration are taken into account, an image restoration cost function on the basis of energy minimization is provided from the perspective of Bayes conditional probability according to the ideas of high fidelity and good detail retention, the image restoration model is established, and finally, the optimal solution of image restoration can be obtained by virtue of deduction optimization. According to the image restoration method, an observation blurred noise image is input and a corresponding degenerate function is provided, and then an excellent restoration result can be obtained quickly. The image restoration method in combination with the weight factor and the gradient restriction is applicable to processing, such as deblurring, denoising and the like, images of visible light, infrared and the like, and capable of realizing image restoration quickly.

Description

technical field [0001] The invention relates to computer image processing technology, in particular to an image restoration method combined with weight factor and gradient restriction. Background technique [0002] In daily imaging and photography, people often encounter insufficient lighting, which requires a longer exposure time. A negative effect of extended exposure times is image blur caused by hand-held camera shake. In order to obtain a clear image, the commonly used methods include turning on the flash, increasing the sensitivity of the camera or using a tripod. Of course, the purpose of the first two methods is to shorten the exposure time to reduce the impact caused by the shake, but the flash causes the color of the scene to change, Sensitivity adjustments introduce more noise, and tripods are too bulky to carry around. In current SLR cameras, there are many lens compensation methods, but the cost is very high. Using algorithm software to restore images to achi...

AI Technical Summary

Problems solved by technology

Although this method is simple and easy to implement, the noise and ringing effects usually included in the results are serious, so its application is greatly limited

Also, a large number of constrained recovery algorithms work well but run poorly

Therefore, the running speed of the recovery algorithm, that is, the efficiency problem is also a problem that needs to be considered. The usual method is not efficient, runs too slowly, and is difficult to apply.

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