We propose a simple yet effective UHDPromer, a neural discrimination-prompted Transformer, for Ultra-High-Definition (UHD) image restoration and enhancement. Our UHDPromer is inspired by an interesting observation that there implicitly exist neural differences between high-resolution and low-resolution features, and exploring such differences can facilitate low-resolution feature representation. To this end, we first introduce Neural Discrimination Priors (NDP) to measure the differences and then integrate NDP into the proposed Neural Discrimination-Prompted Attention (NDPA) and Neural Discrimination-Prompted Network (NDPN). The proposed NDPA re-formulates the attention by incorporating NDP to globally perceive useful discrimination information, while the NDPN explores a continuous gating mechanism guided by NDP to selectively permit the passage of beneficial content. To enhance the quality of restored images, we propose a super-resolution-guided reconstruction approach, which is guided by super-resolving low-resolution features to facilitate final UHD image restoration. Experiments show that UHDPromer achieves the best computational efficiency while still maintaining state-of-the-art performance on $3$ UHD image restoration and enhancement tasks, including low-light image enhancement, image dehazing, and image deblurring. The source codes and pre-trained models will be made available at https://github.com/supersupercong/uhdpromer.

翻译：本文提出一种简单而有效的UHDPromer，即一种神经判别提示Transformer，用于超高清图像恢复与增强。我们的UHDPromer源于一个有趣的观察：高分辨率与低分辨率特征之间隐式存在神经差异，探索这种差异能够促进低分辨率特征表示。为此，我们首先引入神经判别先验来衡量这些差异，随后将NDP集成到所提出的神经判别提示注意力与神经判别提示网络中。提出的NDPA通过融入NDP重新构建注意力机制，以全局感知有用的判别信息；而NDPN则探索由NDP引导的连续门控机制，以选择性允许有益内容通过。为提升恢复图像的质量，我们提出一种超分辨率引导的重建方法，该方法通过超分辨低分辨率特征来引导最终的超高清图像恢复过程。实验表明，UHDPromer在$3$项超高清图像恢复与增强任务（包括低光照图像增强、图像去雾和图像去模糊）中，在保持最先进性能的同时实现了最佳的计算效率。源代码与预训练模型将在https://github.com/supersupercong/uhdpromer公开提供。