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Báo cáo hóa học: "Learning-Based Nonparametric Image Super-Resolution"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Learning-Based Nonparametric Image Super-Resolution | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 51306 Pages 1-11 DOI 10.1155 ASP 2006 51306 Learning-Based Nonparametric Image Super-Resolution Shyamsundar Rajaram 1 Mithun Das Gupta 2 Nemanja Petrovic 3 and Thomas S. Huang2 1 Beckman Institute University of Illinois at Urbana-Champaign IL 61801 USA 2 Beckman Institute at Urbana-Champaign University of Illinois Urbana IL 61801 USA 3 Siemens Corporate Research Princeton NJ 08540-6632 USA Received 1 December 2004 Revised 19 April 2005 Accepted 25 April 2005 We present a novel learning-based framework for zooming and recognizing images of digits obtained from vehicle registration plates which have been blurred using an unknown kernel. We model the image as an undirected graphical model over image patches in which the compatibility functions are represented as nonparametric kernel densities. The crucial feature of this work is an iterative loop that alternates between super-resolution and restoration stages. A machine-learning-based framework has been used for restoration which also models spatial zooming. Image segmentation is done by a column-variance estimation-based dissection algorithm. Initially the compatibility functions are learned by nonparametric kernel density estimation using random samples from the training data. Next we solve the inference problem by using an extended version of the nonparametric belief propagation algorithm in which we introduce the notion of partial messages. Finally we recognize the super-resolved and restored images. The resulting confidence scores are used to sample from the training set to better learn the compatibility functions. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Restoration plays a major role in most vision-based systems as the inputs in most cases are blurred noisy. Blurred images are a nightmare for any recognition system. Many segmentation algorithms fail when the image is .