Image deblurring using tri-segment intensity prior

Camera shake during exposure often introduces annoying blur of objects and deteriorates image quality. Existing image deblurring algorithms usually use intensity and gradient priors to alleviate the degree of blurring. However, these methods only consider the changes caused by the blur process in the low intensity range, omitting the changes caused by the blur process in the high and middle part of the intensity range. In this paper, we propose an effective blind image deblurring algorithm based on the three segments of intensity prior, i.e., low, middle and high parts. This work is motivated by the observation that the blur process destroys the sparsity of both ends of intensity, and meanwhile shrinks the distance between the two distinct gray levels. A fast numerical scheme is deployed for alternatingly computing the sharp image and the blur kernel using an image pyramid at the stage of kernel estimation. Extensive experiments on both synthetic and real-world blurred images demonstrate that our method performs favorably against the state-of-the-art image deblurring methods.