A MULTILAYER MODEL OF IMAGE SUPER-RESOLUTION IN THE PRESENCE OF INNER-FRAME MOTION OUTLIERS
Accurate image registration is vital in image super-resolution. Existence of outliers, which are defined as data points with different distributional characteristics than the assumed model, will produce erroneous estimates, and then get a terrible result. Considering the outliers, Farsiu et.al. proposed a robust super-resolution method to simple reject the outliers frames but not considering the more complex motion model. This paper presents a new multilayer model of image super-resolution in the presence of inner-frame motion outliers. We first use GLOMO algorithm to separate the low-resolution image as several layers. After identify the motion models of the layers, we calculate them separately, then we can get the accurate image registration of background. At last, we fuse them into a high-resolution image. Experimental results indicate that the proposed method is better than Farsius method in the presence of inner-frame motion outliers.
Super-resolution multilayer model outliers
ZHI ZHANG RUN-SHENG WANG
ATR Lab., National University of Defense Technology, Changsha 410073, China Airforce Equipment Acade ATR Lab., National University of Defense Technology, Changsha 410073, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
2718-2722
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）