Super Resolution Image Reconstruction Based on Image Similarity and Feature Combination

Aiming at the problems of losing detailed information, or generating edge distortion and noise while enhancing details in traditional image reconstruction process, this paper proposes an image super-resolution reconstruction method based on image cross-scale similarity and feature combination. Using image cross-scale similarity, we first apply KNN algorithm to respectively establish the mapping relationship of pixel features and gradient features between the high and the low resolution images. Then, we use the mapping relation of pixel features to reconstruct the high-resolution image containing high-frequency information from the input image. Finally, we use singular value thresholding to obtain the effective high-frequency information of the input image, and use the gradient feature mapping relationship to enlarge the high-frequency information which will be superimposed onto the high-resolution image to generate the final image reconstruction result. We utilize the image segmentation database of UCLA as the experimental data and display the experimental results in the Matlab software under Windows 7. The experimental results show that our method can reconstruct images with rich texture details, clear edges, and significantly enhanced image details. It greatly improves visual effects and objective indicators. Moreover, our method does not need to rely on external databases.