Reflection Removal through Efficient Adaptation of Diffusion Transformers

2025-12-04

AI Summary

Single-image reflection removal remains challenging due to the lack of diverse, physically realistic training data and the difficulty of generalizing across domains without target-domain supervision. Method: This paper proposes a novel physics-guided diffusion Transformer framework. First, it constructs a principled Blender rendering pipeline based on BSDF models to synthesize high-fidelity, diverse reflection-contaminated images. Second, it introduces LoRA-based efficient fine-tuning into the diffusion Transformer architecture, enabling lightweight adaptation of pre-trained models. Third, it achieves cross-dataset zero-shot reflection removal without access to any target-domain data. Results: The method achieves state-of-the-art performance on both in-domain and zero-shot benchmarks, significantly improving transmission-layer reconstruction quality and cross-dataset generalization. Ablations validate the effectiveness and scalability of the synergistic design—combining physically grounded data generation with parameter-efficient fine-tuning.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

We introduce a diffusion-transformer (DiT) framework for single-image reflection removal that leverages the generalization strengths of foundation diffusion models in the restoration setting. Rather than relying on task-specific architectures, we repurpose a pre-trained DiT-based foundation model by conditioning it on reflection-contaminated inputs and guiding it toward clean transmission layers. We systematically analyze existing reflection removal data sources for diversity, scalability, and photorealism. To address the shortage of suitable data, we construct a physically based rendering (PBR) pipeline in Blender, built around the Principled BSDF, to synthesize realistic glass materials and reflection effects. Efficient LoRA-based adaptation of the foundation model, combined with the proposed synthetic data, achieves state-of-the-art performance on in-domain and zero-shot benchmarks. These results demonstrate that pretrained diffusion transformers, when paired with physically grounded data synthesis and efficient adaptation, offer a scalable and high-fidelity solution for reflection removal. Project page: https://hf.co/spaces/huawei-bayerlab/windowseat-reflection-removal-web

Problem

• Removes reflections from single images using diffusion transformers
• Addresses data scarcity with physically-based synthetic reflection generation
• Achieves state-of-the-art performance via efficient LoRA adaptation

Innovation

• Adapting diffusion transformers for reflection removal
• Using physically based rendering to synthesize data
• Applying LoRA-based efficient model adaptation