23 research outputs found
Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement
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