Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement
This work addresses the problem of enhancing low-light images for computer vision applications, representing an incremental improvement with a novel hybrid approach.
The paper tackles low-light image enhancement by proposing SPGAT, a method that uses a structural prior guided generative adversarial transformer to restore clear images, and it demonstrates favorable performance against state-of-the-art methods on synthetic and real-world datasets.
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.