To address the challenges of prolonged localization time, poor image quality, and low feature matching accuracy in low-light indoor visual localization, an indoor visual localization algorithm based on SFM (structure-from-motion) and low-light image enhancement was proposed. In the offline phase, image data were collected via video recording. The essential matrix was decomposed in the SFM framework to obtain the relative poses between image pairs, enabling the reconstruction of an initial point cloud. The PnP (perspective-n-point) algorithm was then applied to estimate the relative poses of all images with respect to an initial reference point, yielding their position and scale information. An offline database consisting of images, positions, and scale information was constructed. During the online phase, test images were captured and enhanced using a denoising diffusion model based on kernel KANs (adaptive networks), referred to as KANDiff. The enhanced images were then used for localization via the PnP algorithm, and the real-time user position was estimated using the computed scale. Experimental results demonstrated that the proposed method achieved an average localization error of 0.17 meters. It is concluded that localization accuracy under low-light conditions can be significantly improved using the proposed approach.