An Image Deblurring Method Based on Improved Dark Channel Prior

Abstract

Abstract The traditional dark channel priori has been successfully applied to the single image deblurring problem. According to the characteristics of the dark channel priori, clear images are recovered. However, when the fuzzy image has significant noise pollution, the dark channel prior can not play a role in the fuzzy kernel estimation. The new image denoising method based on the rational order differential inherits the advantages of the total variation denoising method which greatly improves the high frequency part of the image and the fractional order differential denoising method which can well retain the texture details of the image. In this paper, the theory of rational order differential calculation is combined with the dark channel priori of fuzzy image, and an image deblurring method based on the improved dark channel priori is proposed. The specific work is as follows: combining the maximum posterior estimation algorithm and the rational order dark channel prior, a fuzzy image model is constructed; furthermore, the model is solved by using the semi quadratic splitting method. Finally, the multi-scale iterative framework is used to estimate the fuzzy kernel of the accurate image, and then the new non blind image deblurring algorithm can be used to solve the clear image. Experimental results show that the method is effective.