Images taken under low-light conditions are affected by low visible light and noise, which reduce the visual quality and also result in loss of important information.This article proposed a low light image enhancement method that combined smooth clustering and the improved Retinex algorithm to estimate images taken under low-light conditions. An image was separated into the detail layer and the base layer via smooth clustering.Then, max-RGB was used to find the maximum value of each channel to estimate the value of each pixel, construct the initial illumination map, and optimize this map based on local consistency and alternating direction minimization techniques. Adaptive Gamma correction performed non-linear relabeling on the optimized illumination map,providing the final illumination map.The input image could be enhanced by using the information of the final illumination map, and the enhanced image was fused with the detail layer to obtain a clearer and more detailed image.The proposed model exhibited better performance compared with the LE algorithm, GC algorithm, HE algorithm, SSR algorithm, MSR algorithm, MSRCR algorithm, and MSRCP algorithm: the edge intensity is 1.00e + 02, average gradient is 10.520 6, and spatial frequency is 52.050 8. The highest image definition achieved is 14.656 2, which is superior to other algorithms considered in this study, in both subjective and objective evaluations. The experimental results show that the proposed algorithm can generate imageswith higher definition, clearer edges, and richer textures.