tailieunhanh - Báo cáo hóa học: "Research Article Robust Color Image Superresolution: An Adaptive M-Estimation Framework"

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 Robust Color Image Superresolution: An Adaptive M-Estimation Framework | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2008 Article ID 763254 12 pages doi 2008 763254 Research Article Robust Color Image Superresolution An Adaptive M-Estimation Framework Noha A. El-Yamany and Panos E. Papamichalis Department of Electrical Engineering School of Engineering Southern Methodist University . Box 750338 Dallas TX 75275 USA Correspondence should be addressed to Noha A. El-Yamany elyamany@ Received 2 August 2007 Revised 19 November 2007 Accepted 7 February 2008 Recommended by Shoji Tominaga This paper introduces a new color image superresolution algorithm in an adaptive robust M-estimation framework. Using a robust error norm in the objective function and adapting the estimation process to each of the low-resolution frames the proposed method effectively suppresses the outliers due to violations of the assumed observation model and results in color superresolution estimates with crisp details and no color artifacts without the use of regularization. Experiments on both synthetic and real sequences demonstrate the superior performance over using the L2 and L1 error norms in the objective function. Copyright 2008 N. A. El-Yamany and P. E. Papamichalis. 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 Image super-resolution SR is a popular research area for producing high-resolution HR images with better details. The approach taken is to combine the information in a sequence of low-resolution LR images which have subpixel shifts with respect to each other. Most image SR algorithms assume a mathematical model for the imaging process which could have generated the sequence of LR frames from the unknown HR image. However these models are only approximations to reality and model violations often occur because of the .