Super-Resolution Reconstruction from Single Image Based on Join Operation in Granular Computing

Author:

Liu Hongbing1,Zhou Wenyong1,Ma Xuewen1,Wu Chang-An1

Affiliation:

1. Center of Computing, Xinyang Normal University, Xinyang 464000, P. R. China

Abstract

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.

Funder

Natural Science Foundation of China

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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2. Single-Image Super-Resolution based on Steering Kernel and Gaussian Process Regression;International Journal of Pattern Recognition and Artificial Intelligence;2020-10-14

3. Multi-Frame Super-Resolution Reconstruction Algorithm of Optical Remote Sensing Images Based on Double Regularization Terms and Unsupervised Learning;International Journal of Pattern Recognition and Artificial Intelligence;2020-07-27

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