@inproceedings{94e865b8fc054bd48aae816b09730a70,
title = "Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images",
abstract = "Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality. During the image inpainting, we divide preprocessed input images into different branches based on the shadow width to harvest complementary information from different scales. Finally, a sparse representation-based regularizing module is designed to refine the generated contents after multi-scale feature aggregation. Experiments are conducted to compare our proposal versus both traditional and deep learning-based techniques on synthetic and real-world shadows. Results demonstrate that our proposed method achieves favorable image inpainting in terms of visual quality and quantitative metrics, especially when wide shadows are presented.",
keywords = "artifacts removal, image inpainting, retinal optical coherence tomography, shadow removal, sparse representation",
author = "Yaoqi Tang and Yufan Li and Hongshan Liu and Jiaxuan Li and Peiyao Jin and Yu Gan and Yuye Ling and Yikai Su",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE; Medical Imaging 2022: Image Processing ; Conference date: 21-03-2021 Through 27-03-2021",
year = "2022",
doi = "10.1117/12.2611281",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
editor = "Olivier Colliot and Ivana Isgum and Landman, {Bennett A.} and Loew, {Murray H.}",
booktitle = "Medical Imaging 2022",
}