Abstract
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 this https URL.
Abstract (translated)
我们提出了一种简单而有效的UHDPromer，这是一种神经区分提示Transformer，用于超高清（UHD）图像的恢复和增强。我们的UHDPromer受到一个有趣观察的启发，即高分辨率特征与低分辨率特征之间隐含地存在神经差异，并且探索这些差异可以促进低分辨率特征的表现形式。为此，我们首先引入了神经区分先验（NDP），用于衡量这些差异，然后将NDP整合到提出的神经区分提示注意机制（NDPA）和神经区分提示网络（NDPN）中。所提出的NDPA通过融合NDP重新定义注意力机制，以全局感知有用的区分信息，而NDPN则探索一种由NDP指导的连续门控机制，以便选择性地允许有益内容通过。为了提升恢复图像的质量，我们提出了一种超分辨率引导重建方法，该方法受低分辨率特征超解析过程的引导，从而促进最终的UHD图像恢复。 实验结果表明，UHDPromer在计算效率上表现出最佳水平，并且仍能在三个UHD图像恢复和增强任务（包括低光图像增强、去雾化和去除模糊）中保持最先进的性能。源代码及预训练模型可在上述网址获取。
URL
https://arxiv.org/abs/2603.00853
