Abstract
Super-resolution reconstruction is the problem that we want to change the scale low-resolution images and videos to high-resolution. Frankly speaking, the main problem of super-resolution is eliminating the blurring effect like the motion blur, sampling errors, and noisy signal, etc. There are many ways to enhance the image resolution. One of the new algorithms is sparse coding algorithm which is the compressed sensing method and denoised the image reconstruction at first [2]. Since the sparse representation can represent the image patches well from the previous approaches, for recent years, the sparse coding solved the error signal based on the image formula process model and applied to single image super-resolution. But the conventional learning based super-resolution necessaries sufficient training data number of dictionary which means huge codebook size. Even the sparse coding only requires the dictionary with few atoms (basis) than the conventional approach but the testing time is long as the conventional approach. Therefore, we proposed a novel method to solve the single image super-resolution using the K-SVD method to generate the dictionary of the sparse coding with K-means clustering to overcome the problem of computational complexity. At learning phase, we gathered similar patches for proper class, the results shows the proposed method provides the fine quality for viewers. In addition, we apply the combination region of the gradient magnitude and saliency map to make the patch-based reconstruction step reduce the computation time.