Abstract
For image up-scaling, blurs and ringing at the edges or contours of the high fre- quency components in the super resolution image is one of the major problems. The local-self-example image up-scaling method observe that small patches are similar to themselves upon small scaling factors so they use small patch search to reconstruct the high frequency components. Since the high frequency components contain mainly the contour and edge information, we propose a gradient based searching approach that utilizes the features of the high frequency components to extract similar patches. In this paper we observe the distribution of example patches' distribution at rst and based on the result we proposed an distribution based bilateral lter to sharp the edge region and this approach can have less computation time but have similar result since the search process is removed. On the other side, we present another 1-D and 2-D based gradient search for recon- structing the high frequency components in the super resolution image. The 2-D gradient based search takes eight-direction derivations into consideration so that most edge variations are well preserved, the found patches have high structure similarity between the original image and the up-scaling image. Besides, we also proposed a selection mechanism to avoid the mismatching patch error and achieve better performance. Compared with other recent methods, the reconstructed high frequency components of our proposed methods maintain more edge and contour details. our proposed approaches have better PSNR and SSIM values than other approach.