tailieunhanh - Báo cáo hóa học: " Research Article Ordinal Regression Based Subpixel Shift Estimation for Video 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: Research Article Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 85963 9 pages doi 2007 85963 Research Article Ordinal Regression Based Subpixel Shift Estimation for Video Super-Resolution Mithun Das Gupta 1 Shyamsundar Rajaram 1 Thomas S. Huang 1 and Nemanja Petrovic2 1 Department of Electrical and Computer Engineering University of Illinois Urbana Champaign IL 61801-2918 USA 2 Google Inc. 1440 Broadway New York NY 10018 USA Received 2 October 2006 Accepted 3 May 2007 Recommended by Richard R. Schultz We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video superresolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework. The ranking formulation is a variant of classification and regression formulation in which the ordering present in class labels namely the shift between patches is explicitly taken into account. Finally we demonstrate the applicability of our approach on superresolving synthetically generated images with global subpixel shifts and enhancing real video frames by accounting for both local integer and subpixel shifts. Copyright 2007 Mithun Das Gupta et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Shift estimation between two or more frames from a video has been of constant interest to researchers in computer vision. Need for accurate shift estimation arises from many practical situations. Applications such as video frame registration resolution enhancement super-resolution and optical-flow-based tracking depend on reliable techniques for shift estimation for accuracy. Consequently the accuracy of shift estimation methods is of utmost importance for these applications. Since the Lucas-Kanade 1 algorithm

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