SIMRET: A Similarity-Guided Retinex Approach for Low-Light Enhancement

Standard Retinex-based algorithms typically rely on gradient constraints to decompose an image, assuming that illumination is spatially smooth while reflectance contains sharp details. However, strictly gradient-based priors frequently produce halo artifacts or over-smoothing because they are unable to differentiate between intrinsic structural edges and high-frequency noise. In this paper, we propose a novel Similarity Image-Guided Retinex (SIMRET) model that fundamentally diverges from traditional derivative-based regularization. We present a color-based pixel-level similarity analysis to build a global guidance matrix rather than merely depending on local gradients. This Similarity Image functions as a reliable weight map during the decomposition process by mathematically encoding the chromatic relationships and spatial coherence between pixels. The model strictly maintains consistency across structural boundaries to avoid halo effects while adaptively enforcing smoothness in homogeneous regions to suppress noise by incorporating this similarity guidance into the optimization objective. We solve the proposed SIMRET model using an alternating optimization framework, where the similarity constraints effectively regularize the ill-posed decomposition problem. Extensive tests on various low-light datasets show that the suggested model successfully overcomes the trade-off between noise reduction and detail preservation, achieving better visual naturalness and signal fidelity than state-of-the-art techniques.