Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably and Efficiently

Blind Deconvolution of Timely Correlated Sources by Gradient Descent Search

In multichannel blind deconvolution (MBD) the goal is to calculate possibly scaled and delayed estimates of source signals from their convoluted mixtures, using approximate knowledge of the source characteristics only. Nearly all of the solutions to MBD proposed so far require from source signals to be pair-wise statistically independent and to be timely not correlated. In practice, this can on...

Variational blind deconvolution of multi-channel images

The fundamental problem of de-noising and de-blurring images is addressed in this study. The great difficulty in this task is due to the ill-posedness of the problem. We suggest to analyze multi-channel images to gain robustness and to regularize it by the Polyakov action which provides an anisotropic smoothing term that use intra-channel information. Blind de-convolution is then solved by addi...

Optimal Dictionaries for Sparse Solutions of Multi-frame Blind Deconvolution

Abstract: Sparse representation of data has grown rapidly in signal processing. The benefits of sparse regularization are economy of representation of many different varieties of data, as well as control of difficult aspects of inverse problems, e.g., regularization of ill-conditioned inverse problems. Herein we represent atmospheric turbulence point-spread-functions by training optimal overcom...

Modified Sparse Multichannel Blind Deconvolution

Robust Blind Deconvolution via Mirror Descent

We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties. Provable robustness to noise and other perturbations is receiving recent interest in vision, from obtaining immunity to adversarial attacks to assessing and describing failure modes of algorithms in mission critical applications. Further, many blind deconvolution methods based on ...