Efficient Dark Channel Prior Based Blind Image De-blurring

Abstract

Dark channel prior for blind image de-blurring has attained considerable attention in recent past. An interesting observation in blurring process is that the value of dark channel increases after averaging with adjacent high intensity pixels. Lo regularization is proposed to curtail the value of dark channel. Half quadratic splitting method is used to solve the non-convex behavior of Lo regularization. Furthermore, Discrete Wavelet Transform has been incorporated prior to de-blurring to increase the efficiency of algorithm. The most significant finding of this paper is a universal blind image de-blurring algorithm with reduced computational complexity. Experiments are performed and their results are comparable with state of the art de-blurring methods to evaluate the performance of algorithm. Experimental results also reveals that wavelet based dark channel prior image de-blurring is efficient for both uniform and nonuniform blur

Keywords

• dark channel prior
• discrete wavelet transform (dwt)
• latent image
• blur kernel
• sparsity