Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6938225 | Journal of Visual Communication and Image Representation | 2018 | 28 Pages |
Abstract
We propose a super-resolution image reconstruction method using multi-source low resolution images. The proposed method includes a hierarchical structure that combines a neighborhood expansion process with the surface fitting technique. In the proposed method, a series of nested neighborhoods are created to collect LR pixels, and a purification algorithm is put forward to remove the outliers. Then we fit with a surface in each neighborhood to obtain a value at the location of estimated high resolution grid site. These values are pooled to a MAP frame to reconstruct high resolution pixels. Therefore, a reconstructed pixel is associated with the pixel correlation, pixel intensity and the spatial structure. Moreover, our method is non-iterative and does not suffer from convergence problem. Comparing with the state-of-the-art schemes, the proposed method provides superior effect and computational efficiency. Experimental results demonstrate the superiority of the proposed method in both visual fidelity and numerical measures.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Xiaofeng Wang, Didong Zhou, Nengliang Zeng, Xina Yu, Shaolin Hu,