A MAP algorithm to super-resolution image reconstruction

• Published in: Third International Conference on Image and Graphics : proceedings : 18-20 December, 2004, Hong Kong, China pp. 544 - 547
• Main Authors: Huanfeng Shen, Pingxiang Li, Liangpei Zhang, Yindi Zhao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2004
• Subjects: Image reconstruction; Image resolution; Laboratories; Maximum a posteriori estimation; Optical noise; Pixel; PSNR; Reconstruction algorithms; Remote sensing; Testing
• ISBN: 0769522440; 9780769522449
• DOI: 10.1109/ICIG.2004.8

Super-resolution image reconstruction has been one of the most active research areas in recent years. In this paper, a new super-resolution algorithm is proposed to the problem of obtaining a high-resolution image from several low-resolution images that have been sub-sampled and displaced by different amounts of sub-pixel shifts. The algorithm is based on the MAP framework, solving the optimization by proposed iteration steps. At each iteration step, the regularization parameter is updated using the partially reconstructed image solved at the last step. The proposed algorithm is tested on synthetic images, and the reconstructed images are evaluated by the PSNR method. The results indicate that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.