Correlation Matching Transformation Transformers for UHD Image Restoration
Cong Wang, Jinshan Pan, Wei Wang, Gang Fu, Siyuan Liang, and Mengzhu Wang, Xiao-Ming Wu, Jun Liu

TL;DR
UHDformer is a Transformer-based model designed for UHD image restoration, utilizing dual learning spaces and novel modules to enhance feature representation and achieve high performance with significantly reduced model size.
Contribution
The paper introduces UHDformer with dual high- and low-resolution learning spaces and novel modules DualCMT and ACM for improved feature transformation and modulation.
Findings
Reduces model size by about 97% compared to state-of-the-art methods.
Significantly improves performance on UHD image restoration tasks.
Effective in low-light enhancement, dehazing, and deblurring.
Abstract
This paper proposes UHDformer, a general Transformer for Ultra-High-Definition (UHD) image restoration. UHDformer contains two learning spaces: (a) learning in high-resolution space and (b) learning in low-resolution space. The former learns multi-level high-resolution features and fuses low-high features and reconstructs the residual images, while the latter explores more representative features learning from the high-resolution ones to facilitate better restoration. To better improve feature representation in low-resolution space, we propose to build feature transformation from the high-resolution space to the low-resolution one. To that end, we propose two new modules: Dual-path Correlation Matching Transformation module (DualCMT) and Adaptive Channel Modulator (ACM). The DualCMT selects top C/r (r is greater or equal to 1 which controls the squeezing level) correlation channels from…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Advanced Data Storage Technologies
MethodsAttention Is All You Need · Softmax · Layer Normalization · Linear Layer · Byte Pair Encoding · Label Smoothing · Adam · Residual Connection · Position-Wise Feed-Forward Layer · Multi-Head Attention
