Generalized inertial proximal deblurring
Visual signal deblurring is a challenging computational problem involving spatially invariant point spread functions, large blurring matrices and deconvolution. We formulate the visual content restoration process as an inverse convex minimization problem. We design a novel iterative multi-steps scheme incorporating an inertial term to approximate an element of the set of solutions of accretive inclusion problems. We generalize our solver for a large variety of inverse problems in imaging such as convex minimization, variational inequality and split feasibility problems. We compare the convergence rate and perceptual quality assessment with state-of-the-art algorithms on various visual input data. We demonstrate the effectiveness of our solver to deblur RGB images, HDR images, height fields, geometry images as well as motion caption data.
History
Author affiliation
College of Science & Engineering Comp' & Math' SciencesVersion
- AM (Accepted Manuscript)