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High-resolution image reflection removal by Laplacian-based component-aware transformer.

Songnan Chen^1,2, Zhaoxu Feng³

¹School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, Hubei, China.

Scientific Reports

|March 23, 2025

Summary

This study introduces LapCAT, a novel transformer framework for high-resolution image reflection removal. LapCAT effectively removes reflections from detailed images, outperforming existing methods in both quality and efficiency.

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Deep learning methods have advanced image reflection removal but struggle with high-resolution images due to computational demands.
Existing techniques often downsample images, leading to information loss and reduced effectiveness for detailed reflection removal.

Purpose of the Study:

To propose a novel transformer-based framework, LapCAT, for effective high-resolution image reflection removal.
To address the limitations of current methods in handling computational complexity and preserving information in high-resolution images.

Main Methods:

LapCAT utilizes a Laplacian pyramid network for high-frequency reflection pattern removal and high-resolution background reconstruction.
A component-separable transformer block (CSTB) with reflection-aware multi-head self-attention is employed, guided by reflection masks via pixel-wise contrastive learning.

Main Results:

LapCAT demonstrates superior performance in high-resolution image reflection removal compared to state-of-the-art methods.
The framework achieves excellent efficiency and preserves high-resolution details during the reflection removal process.

Conclusions:

LapCAT offers a significant advancement in high-resolution image reflection removal, overcoming computational and information loss challenges.
The proposed method provides a more effective and efficient solution for real-world applications requiring pristine high-resolution imagery.