Survey on Single-Image Reflection Removal using Deep Learning Techniques

TL;DR

This survey comprehensively reviews recent deep learning methods for single-image reflection removal, highlighting key techniques, datasets, challenges, and future opportunities in this rapidly evolving field.

Contribution

It provides a detailed summary of recent work, discusses current techniques and datasets, and identifies key challenges and future directions in deep learning-based reflection removal.

Findings

Deep learning approaches have significantly advanced reflection removal.

Current datasets and evaluation metrics are critical for progress.

Key challenges include handling real-world reflections and generalization.

Abstract

The phenomenon of reflection is quite common in digital images, posing significant challenges for various applications such as computer vision, photography, and image processing. Traditional methods for reflection removal often struggle to achieve clean results while maintaining high fidelity and robustness, particularly in real-world scenarios. Over the past few decades, numerous deep learning-based approaches for reflection removal have emerged, yielding impressive results. In this survey, we conduct a comprehensive review of the current literature by focusing on key venues such as ICCV, ECCV, CVPR, NeurIPS, etc., as these conferences and journals have been central to advances in the field. Our review follows a structured paper selection process, and we critically assess both single-stage and two-stage deep learning methods for reflection removal. The contribution of this survey is three-fold: first, we provide a comprehensive summary of the most recent work on single-image reflection removal; second, we outline task hypotheses, current deep learning techniques, publicly available datasets, and relevant evaluation metrics; and third, we identify key challenges and opportunities in deep learning-based reflection removal, highlighting the potential of this rapidly evolving research area.