Survey on Single-Image Reflection Removal using Deep Learning Techniques

2025-02-12

AI Summary

Reflection interference severely limits the practical applicability of single-image reflection removal. This paper systematically surveys over one hundred deep learning methods published at top-tier computer vision conferences (e.g., CVPR, ICCV) from 2014 to 2024. We propose, for the first time, a unified analytical framework integrating *task assumptions*, *datasets*, and *evaluation metrics*, enabling structured comparison of single-stage versus two-stage architectures. We evaluate end-to-end models—based on CNNs, GANs, and Transformers—on both synthetic (e.g., Real20) and real-world datasets (e.g., SIR2), using multi-dimensional metrics including PSNR, SSIM, and UIQI to characterize performance ceilings and generalization bottlenecks. Our core contributions are threefold: (1) a novel structured taxonomy for reflection removal methods; (2) identification and formalization of two fundamental challenges—robust reflection separation and unpaired degradation modeling; and (3) theoretical guidance and practical roadmaps for algorithm design and benchmark development.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Deep Neural Architectures and Foundation Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Survey explores single-image reflection removal methods.
• Compares traditional and deep learning techniques effectiveness.
• Identifies challenges in deep learning-based reflection removal.

Innovation

• Deep learning-based reflection removal
• Comprehensive literature review
• Single and two-stage methods