Super-Resolution Reconstruction from Single Image Based on Join Operation in Granular Computing
Hongbing Liu, Wenyong Zhou, Xuewen Ma, Chang-an Wu · International Journal of Pattern Recognition and Artificial Intelligence · 2017
Improving the resolution of the image is convenient for people to study the local details of the image, and plays an important role in computer vision. The problem of generating a corresponding super-resolution (SR) image from a single low-resolution (LR) image is addressed via the join operation in the paper. Firstly, the LR image is partitioned into some patches, each patch is represented as the sphere granule set. Secondly, the join operation between two adjacent image patches is used to compensate the pixel value of SR image. Experimental results showed the feasibility and superiority via join operation by root mean square errors (RMSE) between the reconstructed SR image and the original image compared with bicubic interpolation and NNLasso.