Image Super-Resolution via Low-Pass Filter Based Multi-scale Image Decomposition

This paper presents a spatial-varying minimum mean square error (MMSE)-based approach to construct super-resolution images from single source image of a lower resolution. The unique feature of this approach is that it works on a set of sub-images (also called multi-scale images) that are generated via decomposing the original source image. To do the decomposition, we design a number of low-pass filters with overlapped pass-bands so that sub-images are correlated with each other. Then, an MMSE-based estimation, involving all sub-images, is solved (after making use of the geometric-duality principle) to construct each missing pixel in the super-resolution image. Experimental results show that our new method offers a clearly-noticeable improvement over the existing MMSE-based methods (without decomposition). We believe that this is mainly attributing to the fact that both intra-scale and inter-scale correlations among the sub-images have been utilized in our approach.