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

Abstract

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.