DFE-Net: A Dual-Frequency Enhancement Network for Low-Light and Overexposed Image Restoration

In practical imaging applications, low-light and overexposure are two common types of image degradation problems with inherent conflicts, and existing methods struggle to achieve accurate restoration of both degradations within a unified framework. To address this challenge, this paper proposes DFE-Net based on explicit frequency decoupling. The network adopts a symmetric U-Net architecture and embeds discrete wavelet transform (DWT) and inverse discrete wavelet transform (IWT) to construct an explicit dual-frequency processing mechanism, which optimizes the low-frequency information carrying global illumination and the high-frequency information containing detailed textures, respectively. In the encoder, DWT decouples features into low-frequency and high-frequency sub-bands and feeds them into dedicated enhancement modules. The low-frequency enhancement block integrates SS2D and a gated convolutional feed-forward network to efficiently model global contextual dependencies with linear complexity and accurately restore image illumination and contrast; the high-frequency enhancement block adopts CMT attention combined with a matching convolutional feed-forward network, enabling the detail restoration process to be guided by the optimized low-frequency information and ensuring the collaborative optimization of global structure and local textures. The decoder completes the reconstruction and fusion of the processed sub-bands through IWT. The quantitative and qualitative experimental results on the MSEC, SICE, and LOLv1 datasets demonstrate that DFE-Net achieves or surpasses existing state-of-the-art methods in various metrics while maintaining low model complexity.