Problem
Research questions and friction points this paper is trying to address.
Ultra-High-Definition
Image Restoration
Low-Light Image Enhancement
Dehazing
Deblurring
Ultra-High-Definition Restoration Transformers with Correlation Matching Transformation
🤖 AI Summary
提出UHDformer++，一种基于Transformer的框架，通过四个学习空间和两个模块解决超高清图像恢复问题，减少了模型参数并提高了性能。
📝 Abstract
We propose UHDformer++, a general Transformer-based framework to solve numerous Ultra-High-Definition (UHD) image restoration tasks. UHDformer++ operates across $4$ coordinated learning spaces: 1) a high-resolution space (HR) for multi-level feature extraction, 2) a low-resolution space (LR) for learning compact, representative features, 3) a super-resolution space (SR) for upsampling low-resolution features from SR, and 4) a low-high fusion and reconstruction space (LHFR) for final image restoration. Specifically, HR extracts multi-scale high-resolution features and fuses them with low-resolution cues to produce residual images, while LR distills complementary representations from HR to improve restoration quality. To supply LHFR with richer features, SR super-resolves LR outputs before fusion. We further introduce two modules to bridge the high- and low-resolution spaces. The Feature-Refined Correlation Matching Transformation (FR-CMT) module selects the top $C/r~(C~\text{denotes the number of channels;~}r\geq1~\text{controls the squeezing level})$, from the fusion between max- and mean-pooled high-resolution features to replace less informative channels in the low-resolution Transformer. The Adaptive Channel Modulator (ACM) adaptively recalibrates multi-scale high-resolution features, ensuring that only task-relevant information propagates to LR. Extensive experiments demonstrate that UHDformer++ reduces model parameters by at least 86\% compared with recent state-of-the-art methods while achieving substantial performance gains across $5$ UHD restoration tasks, including low-light image enhancement, dehazing, deblurring, deraining, and desnowing. Code will be released at https://github.com/supersupercong/uhdformerplus.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Transformer-based framework
Ultra-High-Definition (UHD) image restoration
Feature-Refined Correlation Matching Transformation (FR-CMT)
Adaptive Channel Modulator (ACM)
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