Translator Disclaimer
26 July 2018 The denoising semi-coupled dictionary learning for retina image super-resolution
Author Affiliations +
Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108280J (2018) https://doi.org/10.1117/12.2501919
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China
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.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jiwen Dong, Weifang Wang, Guang Feng, and Sijie Niu "The denoising semi-coupled dictionary learning for retina image super-resolution", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280J (26 July 2018); https://doi.org/10.1117/12.2501919
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
Back to Top