Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Boundary reconstruction process of a TV-based neural net without prior conditions | Santiago et al. EURASIP Journal on Advances in Signal Processing 2011 2011 115 http content 2011 1 115 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access Boundary reconstruction process of a TV-based neural net without prior conditions Miguel A Santiago1 Guillermo Cisneros1 and Emiliano Bernués2 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. Keywords image restoration neural nets multilayer perceptron MLP boundary conditions BCs image boundary restoration degradation models TV total variation . 1. Introduction Restoration of blurred and noisy images is a classical problem arising in many applications including astronomy biomedical imaging and computerized tomography 1 . This problem aims to invert the degradation because of a capture device but the underlying process is mathematically ill posed and leads to a highly noise sensitive solution. A large number of techniques have been developed to cope with this issue most of them under the regularization or the Bayesian frameworks a complete review can be found in 2-4 . The degraded image is generally modeled as a convolution of the .
