Zero-LED: Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement

Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application. Meanwhile, existing unsupervised methods lack effective bridging capabilities for unknown degradation. To address these limitations, we propose a novel zero-reference lighting estimation diffusion model for low-light image enhancement called Zero-LED. It utilizes the stable convergence ability of diffusion models to bridge the gap between low-light domains and real normal-light domains and successfully alleviates the dependence on pairwise training data via zero-reference learning. Specifically, we first design the initial optimization network to preprocess the input image and implement bidirectional constraints between the diffusion model and the initial optimization network through multiple objective functions. Subsequently, the degradation factors of the real-world scene are optimized iteratively to achieve effective light enhancement. In addition, we explore a frequency-domain based and semantically guided appearance reconstruction module that encourages feature alignment of the recovered image at a fine-grained level and satisfies subjective expectations. Finally, extensive experiments demonstrate the superiority of our approach to other state-of-the-art methods and more significant generalization capabilities. We will open the source code upon acceptance of the paper.

Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion

Low-light image enhancement techniques have significantly progressed, but unstable image quality recovery and unsatisfactory visual perception are still significant challenges. To solve these problems, we propose a novel and robust low-light image enhancement method via CLIP-Fourier Guided Wavelet Diffusion, abbreviated as CFWD. Specifically, we design a guided network with a multiscale visual language in the frequency domain based on the wavelet transform to achieve effective image enhancement iteratively. In addition, we combine the advantages of Fourier transform in detail perception to construct a hybrid frequency domain space with significant perceptual capabilities(HFDPM). This operation guides wavelet diffusion to recover the fine-grained structure of the image and avoid diversity confusion. Extensive quantitative and qualitative experiments on publicly available real-world benchmarks show that our method outperforms existing state-of-the-art methods and better reproduces images similar to normal images. Code is available at https://github.com/He-Jinhong/CFWD.