Image Super-Resolution Framework with Multi-Channel Constraints

Super-resolution reconstruction (SR) has been widely used to produce a high resolution (HR) image from several low resolution (LR) ones. In current methods, a LR image is selected as benchmark and upsampled as the initial SR estimate. This SR estimate is then degraded and compared with the adjacent LR frames for correction. Considering LR images captured from the same HR image with different translation at different instants, SR outputs by different benchmark selection should be identical, so tighter constraints can be designed to limit SR indetermination and to produce better SR images. In this paper, we propose a novel SR framework and prove its efficiency statistically using the unbiased estimation. Experimental results indicate that the proposed algorithm outperforms some existing approaches in both subjective and objective terms.