A L_0 sparse analysis prior for blind poissonian image deconvolution

Bayesian Blind Deconvolution with General Sparse Image Priors

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 additio...

Modified Sparse Multichannel Blind Deconvolution

Blind Deconvolution and Deblurring in Image Analysis

Blind deconvolution problems arise in image analysis when both the extent of image blur, and the true image, are unknown. If a model is available for at least one of these quantities then, in theory, the problem is solvable. It is generally not solvable if neither the image nor the point-spread function, which controls the extent of blur, is known parametrically. In this paper we develop method...

Sparse Space-Time Deconvolution for Calcium Image Analysis

We describe a unified formulation and algorithm to find an extremely sparse representation for Calcium image sequences in terms of cell locations, cell shapes, spike timings and impulse responses. Solution of a single optimization problem yields cell segmentations and activity estimates that are on par with the state of the art, without the need for heuristic preor postprocessing. Experiments o...

Analysis and performance of CMA blind deconvolution for image restoration

In this paper we study the applicability of classical blind deconvolution methods such as constant modulus algorithm (CMA) for blind adaptive image restoration. The requirements such as the source to be white, uniformly distributed and zero mean, which yield satisfactory convergence in the data communication application context, are revisited in the image restoration context, where a linear deb...