Boundary reconstruction process of a TV-based neural net without prior conditions

Abstract

Image restoration aims to restore an image within a given domain from a blurred and noisy acquisition. However, the convolution operator, which models the degradation, is truncated in a real observation causing significant artifacts in the restored results. Typically, some assumptions are made about the boundary conditions (BCs) outside the field of view to reduce the ringing. We propose instead a restoration method without prior conditions which reconstructs the boundary region as well as making the ringing artifact negligible. The algorithm of this article is based on a multilayer perceptron (MLP) which minimizes a truncated version of the total variation regularizer using a back-propagation strategy. Various experiments demonstrate the novelty of the MLP in the boundary restoration process without neither any image information nor prior assumption on the BCs.

Advanced statistical tools for enhanced quality digital imaging with realistic capture models

“…Whereas this is a conceptually correct approach, it usually leads to highly complex (and, thus, somehow unpractical in most cases) restoration algorithms. Santiago et al [17], instead, propose here a novel practical approach based on a multilayer perceptron (the classical neural network), which learns the degradation model. More regular results are produced in the total variation sense, without a priori constraints on the structure of the image or the boundary conditions.…”

“…Santiago et al [17] deal with the problem of boundary handling in image restoration. Despite its practical impact, this problem is often ignored in the restoration field, as evidenced by artificial boundary conditions such as circular or mirror-like boundary conditions used in typical simulation studies.…”