Multi-scale Sparse Representation-Based Shadow Inpainting for Retinal OCT Images

TL;DR

This paper introduces a multi-scale shadow inpainting framework for retinal OCT images that combines sparse representation and CNNs to effectively restore shadowed regions, especially large ones, with improved quality and efficiency.

Contribution

The novel multi-scale framework integrates sparse representation with deep learning to enhance shadow inpainting in retinal OCT images, addressing dataset size and computational challenges.

Findings

Outperforms traditional and deep learning methods in visual quality

Effective for wide shadow regions in OCT images

Achieves better quantitative metrics on synthetic and real data

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.