Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset

Vector meson production and nucleon resonance analysis in a coupled-channel approach for energies m_N < sqrt(s) < 2 GeV II: photon-induced results

We present a nucleon resonance analysis by simultaneously considering all pion- and photon-induced experimental data on the final states gamma N, pi N, 2 pi N, eta N, K Lambda, K Sigma, and omega N for energies from the nucleon mass up to sqrt(s) = 2 GeV. In this analysis we find strong evidence for the resonances P_{31}(1750), P_{13}(1900), P_{33}(1920), and D_{13}(1950). The omega N production mechanism is dominated by large P_{11}(1710) and P_{13}(1900) contributions. In this second part we present the results on the photoproduction reactions and the electromagnetic properties of the resonances. The inclusion of all important final states up to sqrt(s) = 2 GeV allows for estimates on the importance of the individual states for the GDH sum rule.Comment: 41 pages, 26 figures, discussion extended, typos corrected, references updated, to appear in Phys. Rev.

MRF-Based blind image deconvolution

International audienceThis paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method1 by applying it on a wide variety of very challenging cases that involve the inference of large and complicated blur kernels

Background recovery by fixed-rank robust principal component analysis

10.1007/978-3-642-40261-6_6Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)8047 LNCSPART 154-6

Bayesian Blind Deconvolution with General Sparse Image Priors

Abstract. We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.