Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior
This addresses the challenge of removing complex blur in photography for applications like image restoration, though it appears incremental as it builds on existing Bayesian and convex analysis frameworks.
The paper tackles the problem of non-uniform blind deblurring from camera shake, which involves estimating spatially-varying blur operators, and presents a novel algorithm that automatically adjusts its penalty based on local blur and image structure, achieving competitive results on real images.
Typical blur from camera shake often deviates from the standard uniform convolutional script, in part because of problematic rotations which create greater blurring away from some unknown center point. Consequently, successful blind deconvolution requires the estimation of a spatially-varying or non-uniform blur operator. Using ideas from Bayesian inference and convex analysis, this paper derives a non-uniform blind deblurring algorithm with several desirable, yet previously-unexplored attributes. The underlying objective function includes a spatially adaptive penalty which couples the latent sharp image, non-uniform blur operator, and noise level together. This coupling allows the penalty to automatically adjust its shape based on the estimated degree of local blur and image structure such that regions with large blur or few prominent edges are discounted. Remaining regions with modest blur and revealing edges therefore dominate the overall estimation process without explicitly incorporating structure-selection heuristics. The algorithm can be implemented using a majorization-minimization strategy that is virtually parameter free. Detailed theoretical analysis and empirical validation on real images serve to validate the proposed method.