Application of different feature weights based on learning feature dictionary for image super-resolution

Abstract

In this paper, we propose a method of feature differential application based on dictionary data structure for the generation of a super-resolution image in a single image. The existing method of generating super-resolution based on the dictionary data structure results in poor quality, such as artifacts or the staircase. because it refers to the value of the dictionary data without analyzing the configuration of each area. In order to overcome this problem, the proposed method generates a low-resolution image for the dictionary data construction and constructs a pair of dictionary data of low resolution and high resolution through feature extraction with the original image. In order to differentially apply the dictionary features, we estimated the feature loss area in the bicubic interpolation process and analyzed the composition of the details of the area, then weighed it. Using the calculated weight values, we applied the feature data of the dictionary data to each region differentially in order to generate an improved super-resolution image. For experimentation, the original image was compared with the reconstructed image with PSNR and SSIM.