Paper
23 August 2024 Adaptive fusion super-resolution based on parallel scoring
Cong Peng, Yupin Luo
Author Affiliations +
Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 1325026 (2024) https://doi.org/10.1117/12.3038565
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia
Abstract
Super-resolution image reconstruction refers to the estimation of high-resolution image from low-resolution input image. The reconstruction effect depends heavily on the relevance between the external training set and the test image. In this paper, we propose a novel fusion SR method, which makes use of the self-similar property of internal samples, so that the ill-condition external training set can also reconstruct the image robustly. Specifically, we define relevance quantitative index and calculate relevance scores for parallel feature channels. Based on the principle of consistency maximization of low correlation regions, we reasonably synthesize the results of parallel scores, classify image patches, and determine the fusion proportion of internal and external samples adaptively. Experimental results show that the proposed algorithm can effectively alleviate the visual defects caused by ill-conditioned training set. It is worth noting that under the condition of insufficient training samples, our method perform better than the reconstruction method based on deep learning.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Cong Peng and Yupin Luo "Adaptive fusion super-resolution based on parallel scoring", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 1325026 (23 August 2024); https://doi.org/10.1117/12.3038565
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KEYWORDS
Reconstruction algorithms

Education and training

Image restoration

Image fusion

Image classification

Visualization

Machine learning

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