Image Super-Resolution Reconstruction Based On Multi-Dictionary Learning

In order to overcome the problems that the single dictionary cannot be adapted to variety types of images and the reconstruction quality couldn’t meet the application, we propose a novel Multi-Dictionary Learning algorithm for feature classification. The algorithm uses the orientation information of the low resolution image to guide the image patches in the database to classify, and designs the classification dictionary which can effectively express the reconstructed image patches. Considering the nonlocal similarity of the image, we construct the combined nonlocal mean value(C-NLM) regularizer, and take the steering kernel regression(SKR) to formulate a local regularization ,and establish a unified reconstruction framework. Extensive experiments on single image validate that the proposed method, compared with several other state-of-the-art learning based algorithms, achieves improvement in image quality and provides more details.