Anisotropic pth-order TV-based Retinex decomposition with adaptive reflectance regularizer for low-light image enhancement

Image enhancement plays a fundamental role in image processing and computer vision. Its primary purpose is to improve the visual quality of an image by enhancing its contrast and brightness. However, most existing enhancement methods tend to amplify the imaging noise, especially in very dark regions of the image, leading to undesirable artifacts in the enhanced result. To address this problem, this paper aims to develop a method that enhances low-light images without introducing these artifacts. We propose a novel anisotropic $p$th-order total variation-based (ApTV-based) Retinex decomposition with an adaptive reflectance regularizer for low-light image enhancement, where $p$ represents the exponent in our regularization term, controlling the degree of structure preservation in the resulting image. Specifically, for $0<p\le 1$, the ApTV with a smaller $p$-value can effectively extract strong structures of the image, making it suitable for piecewise smooth illumination estimation. In contrast, a larger $p$-value can help preserve the image’s fine details and suppress noise, making it favorable for accurate reflectance estimation. More importantly, since the degree of noise amplification varies across different regions, we incorporate the obtained illumination into the reflectance regularizer to enable adaptive denoising. Extensive numerical experiments and comparisons with state-of-the-art low-light image enhancement methods demonstrate that the proposed adaptive Retinex decomposition approach achieves superior performance both qualitatively and quantitatively. It effectively addresses noise amplification and artifact issues while enhancing overall image quality.