P‐5.3: A super resolution reconstruction algorithm based on spatial autoregression regularization

• Published in: SID International Symposium Digest of technical papers Vol. 49; no. S1; pp. 584 - 588
• Main Authors: shen, Huaming, Xu, Meihua, Ran, Feng, Li, Liming
• Format: Journal Article
• Language: English
• Published: Campbell Wiley Subscription Services, Inc 01.04.2018
• Subjects: Adaptive control; Algorithms; autoregressive; Image degradation; Image reconstruction; low-resolution; micro display; reconstruction; Regression analysis; Regularization
• ISSN: 0097-966X; 2168-0159
• DOI: 10.1002/sdtp.12789

In a lot of micro display applications, we need to enlarge and recognize a region of interest (ROI), which often get a low‐resolution image because of limited resolution source image. This paper proposed a novel image reconstruction algorithm based on spatial autoregression regularization and sparse representation. This reconstruction algorithm trained an image dictionary with the same sparse coefficient by sparse K‐SVD, and then import the autoregressive regularization item to construct the objective function which can realize local adaptive control of the image. In order to obtain further clear image, the degradation model was used which can realize the global constraints. The experimental results show that the proposed algorithm has considerable effectiveness in terms of both objective measurements and visual evaluation.