  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.
