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The denoising semi-coupled dictionary learning for retina image super-resolution
Author(s): Jiwen Dong; Weifang Wang; Guang Feng; Sijie Niu
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Paper Abstract

Retina images are mainly obtained by Spectral Domain-Optical Coherence Tomography (SD-OCT), however, most of the acquired volume data are low-resolution(LR) images with noise, making it hard to quantify diseased tissue based on low quality retinal images. In this paper, we propose a denoising Semi-Coupled Dictionary Learning(SCDL) model to reconstruct the noise image while guaranteeing certain noise robustness. First, we use non-local similarities of retina images to construct constraint term, which is added to the objective function of the proposed model. Then, in order to guarantee the fidelity of reconstructed image, the initialized interpolation section should be replaced by the corresponding LR image after SR reconstruction. However, the noise in LR image will affects the reconstructed image quality. So we perform bilateral filtering on the LR image before replacement. Last, two sets of experiments on retinal noise images validate that our proposed method outperforms other state-of-the-art methods.

Paper Details

Date Published: 26 July 2018
PDF: 10 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280J (26 July 2018); doi: 10.1117/12.2501919
Show Author Affiliations
Jiwen Dong, Univ. of Jinan (China)
Weifang Wang, Univ. of Jinan (China)
Guang Feng, Univ. of Jinan (China)
Sijie Niu, Univ. of Jinan (China)

Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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