Degradation-Aware One-Step Diffusion Model for Content-Sensitive Super-Resolution in the Dark

Diffusion-based super-resolution methods have achieved impressive results under normal lighting conditions. However, their performance in low-light scenarios faces fundamental limitations due to two inherent challenges. First, the characteristic noise patterns and complex degradation features in severely underexposed images create significant obstacles for diffusion models to establish reliable noise prediction mechanisms. Second, these methods often fail to establish effective coupling between the degradation priors of low-light observations and the reconstruction process, resulting in compromised detail recovery and unrealistic texture synthesis.To address these limitations, we propose Degradation-aware Adaptation with Representation Embedding (DARE) method, a novel one-step diffusion framework specifically designed for super-resolution in dark environments. DARE employs a degradation-aware low-rank adaptation strategy that dynamically adjusts model parameters conditioned on degradation-specific features, effectively addressing compound degradations such as low-light, blur, and noise. Furthermore, we introduce a content-sensitive representation embedding mechanism, integrating complementary spatial and frequency domain priors through a bilinear cross-attention module. This module explicitly captures second-order statistical correlations, enriching semantic understanding and detail recovery during the denoising process. Extensive experiments across diverse low-light scenarios demonstrate that DARE outperforms state-of-the-art methods in terms of both visual quality and perceptual accuracy. The code is available at https://github.com/csmty/DARE.