BID: An Effective Blind Image Deblurring Scheme to Estimate the Blur Kernel for Various Scenarios

In recent years, image deblurring has been widely investigated. In order to solve this ill-posed problem, a variety of prior models have been proposed successively. The two-tone prior has been successfully applied to text images deblurring and achieved significant results. However, the natural image with rich color clusters does not meet the two-tone prior, which requires only two color clusters in the image. In this paper, a local two-tone prior is proposed for images with complex color clusters, which decomposes the image with complex color clusters into patches with simple color clusters. We also find that the process of image blurring is a weighted average of pixel values, which will lead to an increase of intermediate pixel values. Therefore, a new measure of the dynamic range of the image is proposed, which indicates the difference of the color cluster using the average value of the color cluster, and it is rigorously proved in mathematics. Besides, we analyze the effectiveness of the proposed prior in image deblurring in detail. Experimental results on the widely used datasets show that the proposed method performs favorably against the state-of-the-art algorithms, both qualitatively and quantitatively.