  Existing unsupervised low-light image enhancement methods lack enough
effectiveness and generalization in practical applications. We suppose this is
because of the absence of explicit supervision and the inherent gap between
real-world scenarios and the training data domain. In this paper, we develop
Diffusion-based domain calibration to realize more robust and effective
unsupervised Low-Light Enhancement, called DiffLLE. Since the diffusion model
performs impressive denoising capability and has been trained on massive clean
images, we adopt it to bridge the gap between the real low-light domain and
training degradation domain, while providing efficient priors of real-world
content for unsupervised models. Specifically, we adopt a naive unsupervised
enhancement algorithm to realize preliminary restoration and design two
zero-shot plug-and-play modules based on diffusion model to improve
generalization and effectiveness. The Diffusion-guided Degradation Calibration
(DDC) module narrows the gap between real-world and training low-light
degradation through diffusion-based domain calibration and a lightness
enhancement curve, which makes the enhancement model perform robustly even in
sophisticated wild degradation. Due to the limited enhancement effect of the
unsupervised model, we further develop the Fine-grained Target domain
Distillation (FTD) module to find a more visual-friendly solution space. It
exploits the priors of the pre-trained diffusion model to generate
pseudo-references, which shrinks the preliminary restored results from a coarse
normal-light domain to a finer high-quality clean field, addressing the lack of
strong explicit supervision for unsupervised methods. Benefiting from these,
our approach even outperforms some supervised methods by using only a simple
unsupervised baseline. Extensive experiments demonstrate the superior
effectiveness of the proposed DiffLLE.
