论文标题
结构性先验引导的生成对抗变压器，用于弱光图像增强
Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement
论文作者
论文摘要
我们提出了有效的结构性先验引导的生成对抗变压器（SPGAT）来解决低光图像增强。我们的SPGAT主要包含一个具有两个鉴别器和一个结构性估计器（SPE）的发生器。该发电机基于U形变压器，该变压器用于探索非本地信息，以更好地清晰图像恢复。 SPE用于探索图像中有用的结构，以指导发电机以进行更好的结构细节估计。为了生成更真实的图像，我们通过在发生器和歧视器之间建立跳过连接来开发一种新的结构性指导的对手学习方法，以便歧视者可以更好地区分真实功能和虚假特征。最后，我们提出了一个基于Windows的Swin Transformer块，以汇总不同级别的层次特征，以进行高质量的图像恢复。实验结果表明，所提出的SPGAT在合成数据集和现实世界中的最新方法中表现出色。
We propose an effective Structural Prior guided Generative Adversarial Transformer (SPGAT) to solve low-light image enhancement. Our SPGAT mainly contains a generator with two discriminators and a structural prior estimator (SPE). The generator is based on a U-shaped Transformer which is used to explore non-local information for better clear image restoration. The SPE is used to explore useful structures from images to guide the generator for better structural detail estimation. To generate more realistic images, we develop a new structural prior guided adversarial learning method by building the skip connections between the generator and discriminators so that the discriminators can better discriminate between real and fake features. Finally, we propose a parallel windows-based Swin Transformer block to aggregate different level hierarchical features for high-quality image restoration. Experimental results demonstrate that the proposed SPGAT performs favorably against recent state-of-the-art methods on both synthetic and real-world datasets.
