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cs.CV2026
Adapting Large VLMs with Iterative and Manual Instructions for Generative Low-light Enhancement
Xiaoran Sun, Liyan Wang, Yeying Jin +5
Most existing low-light image enhancement (LLIE) methods rely on pre-trained model priors, low-light inputs, or both, while neglecting the semantic guidance available from normal-l…
cs.CV2026
LinearSR: Unlocking Linear Attention for Stable and Efficient Image Super-Resolution
Xiaohui Li, Shaobin Zhuang, Shuo Cao +6
Generative models for Image Super-Resolution (SR) are increasingly powerful, yet their reliance on self-attention's quadratic complexity (O(N^2)) creates a major computational bott…
cs.CV2026
Neural Discrimination-Prompted Transformers for Efficient UHD Image Restoration and Enhancement
Cong Wang, Jinshan Pan, Liyan Wang +2
We propose a simple yet effective UHDPromer, a neural discrimination-prompted Transformer, for Ultra-High-Definition (UHD) image restoration and enhancement. Our UHDPromer is inspi…