  Blind image deblurring algorithms have been improving steadily in the past
years. Most state-of-the-art algorithms, however, still cannot perform
perfectly in challenging cases, especially in large blur setting. In this
paper, we focus on how to estimate a good kernel estimate from a single blurred
image based on the image structure. We found that image details caused by
blurring could adversely affect the kernel estimation, especially when the blur
kernel is large. One effective way to eliminate these details is to apply image
denoising model based on the Total Variation (TV). First, we developed a novel
method for computing image structures based on TV model, such that the
structures undermining the kernel estimation will be removed. Second, to
mitigate the possible adverse effect of salient edges and improve the
robustness of kernel estimation, we applied a gradient selection method. Third,
we proposed a novel kernel estimation method, which is capable of preserving
the continuity and sparsity of the kernel and reducing the noises. Finally, we
developed an adaptive weighted spatial prior, for the purpose of preserving
sharp edges in latent image restoration. The effectiveness of our method is
demonstrated by experiments on various kinds of challenging examples.
