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: Adaptive Markov Random Fields for Example-Based Super-resolution of Faces | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 31062 Pages 1-11 DOI ASP 2006 31062 Adaptive Markov Random Fields for Example-Based Super-resolution of Faces Todd A. Stephenson1 2 and Tsuhan Chen1 1 Electrical Computer Engineering Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh PA 15213-3890 USA 2ReallaeR LLC . Box 549 Port Republic 20676 MD USA Received 21 December 2004 Revised 1 April 2005 Accepted 5 April 2005 Image enhancement of low-resolution images can be done through methods such as interpolation super-resolution using multiple video frames and example-based super-resolution. Example-based super-resolution in particular is suited to images that have a strong prior for those frameworks that work on only a single image it is more like image restoration than traditional multiframe super-resolution . For example hallucination and Markov random field MRF methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be. We propose to use even stronger prior information by extending MRF-based super-resolution to use adaptive observation and transition functions that is to make these functions region-dependent. We show with face images how we can adapt the modeling for each image patch so as to improve the resolution. Copyright 2006 T. A. Stephenson and T. Chen. 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 Early work on enhancing low-resolution images addressed increasing the resolution of the image without any specific outside information related to the image domain. Methods such as linear interpolation 1 first reproduce the existing pixels to produce a magnified image and then smooth the new image. In increasing the .
