Single image super-resolution via adaptive dictionary pair learning for wireless capsule endoscopy image

Wireless capsule endoscopy (WCE) is an innovative solution for gastrointestinal disease detection. Limited by WCE hardware and cost of manufacture, WCE image resolution is commonly low, which creates problems for attention to image details and visual perception in medical diagnosis. Under the sparse representation framework, we propose an adaptive dictionary pair learning method to obtain more appropriate representation of each patch with more relevant atoms according to patch content. Specifically, the dictionary pair is learned from high-low resolution cluster patches based on sparse constraint of input patches. Careful examination of the WCE images show there exist unnatural block areas. In order to further improve performance, the autoregressive model is applied to enhance local structure. Intensive experiments have been conducted on WCE image dataset and natural image dataset, including comparison test between the state-of-art methods and ours, and the results validate the effectiveness of the proposed method both on visual perception effect and objective indices.