TSFormer: A Robust Framework for Efficient UHD Image Restoration
Xin Su, Chen Wu, Zhuoran Zheng

TL;DR
TSFormer is a novel UHD image restoration framework that combines trusted learning and sparsification, enabling real-time processing of 4K images with high quality, robustness, and efficiency.
Contribution
Introduces TSFormer, a framework integrating trusted learning with token sparsification, improving generalization and efficiency in UHD image restoration.
Findings
Achieves real-time 4K image restoration at 40fps
Outperforms existing methods in restoration quality
Reduces computational complexity significantly
Abstract
Ultra-high-definition (UHD) image restoration is vital for applications demanding exceptional visual fidelity, yet existing methods often face a trade-off between restoration quality and efficiency, limiting their practical deployment. In this paper, we propose TSFormer, an all-in-one framework that integrates \textbf{T}rusted learning with \textbf{S}parsification to boost both generalization capability and computational efficiency in UHD image restoration. The key is that only a small amount of token movement is allowed within the model. To efficiently filter tokens, we use Min- with random matrix theory to quantify the uncertainty of tokens, thereby improving the robustness of the model. Our model can run a 4K image in real time (40fps) with 3.38 M parameters. Extensive experiments demonstrate that TSFormer achieves state-of-the-art restoration quality while enhancing…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Image Processing Techniques and Applications
