Low-light images often suffer from low visibility and various noise.
Most existing low-light image enhancement methods often amplify
noise when enhancing low-light images, due to the neglect of separating
valuable image information and noise. In this paper, we
propose a novel wavelet-based attention network, where wavelet
transform is integrated into attention learning for joint low-light
enhancement and noise suppression. Particularly, the proposed
wavelet-based attention network includes a Decomposition-Net, an
Enhancement-Net and a Restoration-Net. In Decomposition-Net, to
benefit denoising, wavelet transform layers are designed for separating
noise and global content information into different frequency
features. Furthermore, an attention-based strategy is introduced
to progressively select suitable frequency features for accurately
restoring illumination and reflectance according to Retinex theory.
In addition, Enhancement-Net is introduced for further removing
degradations in reflectance and adjusting illumination, while
Restoration-Net employs conditional adversarial learning to adversarially
improve the visual quality of final restored results based
on enhanced illumination and reflectance. Extensive experiments
on several public datasets demonstrate that the proposed method
achieves more pleasing results than state-of-the-art methods.