Two-Stage Low-light Image Enhancement Based on Wavelet Transform

To address these issues, this paper proposes a dual-stage wavelet transform-based method for low-light image enhancement. The method builds upon wavelet transform theory and employs a U-Net architecture to progressively achieve feature encoding, decoding, and enhancement through two sequential stages: preliminary restoration and fine-grained enhancement.

For effective noise suppression and detail enhancement, we design an enhanced wavelet-domain feature fusion module that integrates discrete wavelet transform, inverse discrete wavelet transform, and dual attention mechanisms. Meanwhile, the proposed dynamic gated spatial attention and lightweight fusion-curve attention mechanisms collaborate within this feature fusion module to process image features with refined adaptability.