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Efficient Method for High-Quality Removal of Nonuniform Blur in the Wavelet Domain

This paper presents a novel nonuniform deblurring approach, which defines the blur model and calculates regularized nonuniform deconvolution in the wavelet domain to achieve high efficiency and high accuracy simultaneously. Targeting high computation efficiency, we derive a wavelet-domain hierarchical blur model, which can be calculated efficiently by exploiting the sparsity property of natural images in the wavelet domain. Correspondingly, the blur model is incorporated into a multilayer framework and at each layer spatially varying step sizes are introduced to further accelerate the convergence of the algorithm. In addition to the efficiency advantages, the proposed approach deals with intensely nonuniform blur with high accuracy due to the intrinsic tight supportness of wavelet basis. We conduct a series of experiments and comparisons to validate the efficiency and effectiveness of our algorithm.