Mr. Jingbo Ren Profile

Jingbo Ren

at Xidian Univ

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Proceedings Article | 5 November 2014 Paper

Image super-resolution via adaptive filtering and regularization

Jingbo Ren, Hao Wu, Weisheng Dong, Guangming Shi

Proceedings Volume 9273, 92731A (2014) https://doi.org/10.1117/12.2071767

KEYWORDS: Image filtering, Digital filtering, Super resolution, Wavelets, Image quality, Image resolution, Image restoration, Lawrencium, Image fusion, Wavelet transforms

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Image super-resolution (SR) is widely used in the fields of civil and military, especially for the low-resolution remote sensing images limited by the sensor. Single-image SR refers to the task of restoring a high-resolution (HR) image from the low-resolution image coupled with some prior knowledge as a regularization term. One classic method regularizes image by total variation (TV) and/or wavelet or some other transform which introduce some artifacts. To compress these shortages, a new framework for single image SR is proposed by utilizing an adaptive filter before regularization. The key of our model is that the adaptive filter is used to remove the spatial relevance among pixels first and then only the high frequency (HF) part, which is sparser in TV and transform domain, is considered as the regularization term. Concretely, through transforming the original model, the SR question can be solved by two alternate iteration sub-problems. Before each iteration, the adaptive filter should be updated to estimate the initial HF. A high quality HF part and HR image can be obtained by solving the first and second sub-problem, respectively. In experimental part, a set of remote sensing images captured by Landsat satellites are tested to demonstrate the effectiveness of the proposed framework. Experimental results show the outstanding performance of the proposed method in quantitative evaluation and visual fidelity compared with the state-of-the-art methods.

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