This paper proposes a novel Denoiser and Low-Frequency Enhancer Network (DLFE-Net) for Low-Light Image Enhancement (LLIE). The DLFE-Net addresses two key challenges: (1) overexposure and detail loss in local areas during enhancement, and (2) the effective removal of inherent noise in low-light images. Specifically, the input RGB image is first converted to the HVI color space. The intensity (I) and color (H, V) maps are then enhanced and denoised separately, i.e., preserving details and removing noise. For preserving details, the Low-Frequency Illumination Enhancer (LFIE) module isolates and processes the image’s low-frequency information. This targeted approach effectively mitigates local overexposure and preserves fine details during enhancement. For removing noise, the Multi-Scale Gated Denoiser (MSGD) module performs denoising through strong preservation after predicting image noise. Comprehensive experiments were conducted on three benchmark datasets (LOL, SICE, Sony-Total-Dark) and five unpaired datasets. Both qualitative and quantitative analyses demonstrated the superiority of DLFE-Net over state-of-the-art methods. Moreover, ablation studies demonstrated the effectiveness of each module in DLFE-Net.
He et al. (Fri,) studied this question.