Rotation expanded dictionary-based single image super-resolution

Abstract

In this report, issues that affect the performance of the neighbor embedding (NE)-based Super-Resolution (SR) method are analyzed. Effective enrichment of the dictionary is a critical factor for the NE-based SR method. To efficiently enhance the dictionary's expressive capability, a rotation expanded dictionary (RED) incorporating the Radon transform (RT) technique is proposed. By representing patch rotations with a compact scheme, both the search for neighbors and the estimation of rotation angles in the SR process are significantly simplified. To refine the patch matching accuracy when using the expanded dictionary, a new level of imaging, known as the middle-resolution (MR) image, is proposed to replace the original low-resolution (LR) image in patch matching. Because MR patches bear more distinguishable features, this modification is able to identify neighbors more accurately for the input patches. Lastly, the effects of a single image SR method based on the MR matching and the rotation expansion are examined in simulations. A comprehensive comparison with several state-of-the-art SR methods demonstrates the superior performance of the proposed method.