Sparse image restoration using iterated linear expansion of thresholds
Journal
Proceedings - International Conference on Image Processing, ICIP
ISBN
9781457713033
Date Issued
2011-12-01
Author(s)
Pan, Hanjie
Abstract
We focus on image restoration that consists in regularizing a quadratic data-fidelity term with the standard ℓ 1 sparse-enforcing norm. We propose a novel algorithmic approach to solve this optimization problem. Our idea amounts to approximating the result of the restoration as a linear sum of basic thresholds (e.g. soft-thresholds) weighted by unknown coefficients. The few coefficients of this expansion are obtained by minimizing the equivalent low-dimensional ℓ 1-norm regularized objective function, which can be solved efficiently with standard convex optimization techniques, e.g. iterative reweighted least square (IRLS). By iterating this process, we claim that we reach the global minimum of the objective function. Experimentally we discover that very few iterations are required before we reach the convergence. © 2011 IEEE.
Subjects
Image deconvolution | Iterative Shrinkage Threshold (IST) | Linear Expansion of Thresholds (LET) | sparsity | thresholding
Type
conference paper