Modified Diff-Retinex Model for Robust Low-Light Image Enhancement

Universal low-light image restoration based on adaptive enhancement diffusion model

Variational Retinex Model with Illumination Guidance and Fractional Derivative for Low-Light Enhancement

Low-light enhancement is a crucial task in computer vision; it can improve either the subjective experience of viewers or the usability of computer vision systems designed for normal-light images. In this paper, a variational Retinex model in the image domain is developed for low-light enhancement, which infuses classical/fractional differentiation of the input image into the illumination/reflectance component by means of a structure/texture-aware map (SAM/TAM). Firstly, the SAM (TAM) is generated by the inverse square of classical (fractional) differentiation of the input image. Secondly, the regularization term of illumination (reflectance) is defined by utilizing the SAM (TAM) as a weighted matrix, and an illumination guidance term is incorporated into the objective function. The illumination guidance term encourages the estimated illumination to encompass more structural information by penalizing deviation of illumination from the illumination pre-estimated by a dark channel prior to a guided image filtering. Finally, an alternative algorithm is employed to solve the minimization problem involved in the model. The performance of the proposed method is evaluated on three datasets for low-light enhancement and compared with eight state-of-the-art Retinex methods, qualitatively and quantitatively. Evaluation results show that the proposed method generally achieves higher performance in terms of low-light enhancement.

WREN: Low Light Image Enhancement Using Retinex theory-based Double U-Net-like Structures

WREN is an LLIE neural network with double U-Net-like structures trained end-to-end with a scale-invariant loss function, which gives robustness against the illumination scaling, and achieves the state-of-the-art performance across multiple datasets.

Deep white balance and dark channel prior fusion: Toward efficient and natural low-light image restoration

Image enhancement in low-light conditions is a challenging problem within the field of computer vision, since underexposed images generally lead to poor visibility, low contrast, noise amplification, and color distortion. Recent deep learning approaches have indeed shown promising performance, yet most of them adopt computation-heavy architectures and do not treat the luminance enhancement and color restoration separately; as a result cause unnatural restorations. To this end, in this paper, we present an efficient low-light image enhancement framework based on the Deep White-Balance (DWB) with Dark Channel Prior guidance in the YCbCr color space. The proposed framework separates luminance and chrominance components, making it easier to manage brightness enhancement and color restoration separately. This method aims to generate visually consistent enhanced images while preserving color fidelity and avoiding common enhancement artifacts. We evaluate the performance of the proposed method on reference benchmark datasets (LOL, LOLv2-Synthetic, and LIME) as well as a no-reference benchmark dataset (DICM). The experimental results demonstrate that, although state-of-the-art deep learning methods achieve higher numerical scores, qualitatively, the framework produces enhanced images with consistent contrast and illumination, while retaining color fidelity, all requiring less computational resources. Moreover, it does not require any further training or fine-tuning since the proposed approach is based on a pretrained Deep White Balance model and only uses inference. Experimental results show that the proposed method provides a convincing quality-efficiency compromise for low-light image enhancement.

Zero-Shot Low-Light Image Enhancement via Diffusion with Joint Frequency and Spatial Guidance

Existing zero-shot low-light image enhancement methods often underutilize image priors, leading to noise amplification and color distortion. To address these issues, we propose a zero-shot framework for low-light image enhancement. The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone. Specifically, the denoising module uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions. We then guide the reverse sampling of the pre-trained diffusion model with a refinement strategy operating in both the frequency and spatial domains, so that illumination enhancement and local detail refinement can be jointly achieved during sampling. At each step, Fourier-based reconstruction contributes to illumination enhancement while preserving structural information, and illumination-guided spatial adjustment further refines local brightness. Experiments on multiple benchmark datasets show that the proposed method improves illumination while preserving structural details.

Prior-calibrated spatial-frequency Retinex decomposition for unsupervised low-light image enhancement