<p>Low-light image enhancement is an important research direction in the field of digital image processing, aiming to improve the visual quality of images or to support subsequent image processing tasks. The Retinex theory has been widely applied in image processing. To simplify computation, some Retinex models convert the multiplicative decomposition model into an additive decomposition model using logarithmic transformations, which may lead to the loss of texture details in the reflectance map. In this paper, we adopt the original multiplicative decomposition model and construct an objective function with variational characteristics that takes into account both structural and texture perception. A weight matrix based on spatial domain statistical features is introduced in both the structural and texture regularization terms to enhance texture information. The objective function is then decomposed into two convex subproblems, which are alternately solved using the Alternating Direction Method of Multipliers (ADMM). Experimental results show that the generated reflectance map retains richer texture details and achieves effective enhancement of low-light images. Experiments on three commonly used public datasets, along with quantitative and qualitative comparisons with several mainstream advanced algorithms, demonstrate that the proposed method achieves superior performance in low-light image enhancement tasks.</p>