Low-light image enhancement （LLIE） aims to recover visually pleasing images from severely underexposed inputs by improving brightness，contrast，and structural clarity while preserving natural colors and fine details. This task is fundamentally important for numerous downstream vision applications，including video surveillance，autonomous driving，remote sensing，and medical imaging，where low illumination conditions frequently degrade visual quality and impair algorithmic reliability. In recent years，diffusion probabilistic models have demonstrated remarkable generative capacity and robustness，making them a promising paradigm for LLIE. However，directly applying diffusion models to low-light enhancement remains challenging due to several intrinsic limitations. First，the iterative denoising mechanism of diffusion models introduces substantial computational and memory overhead，hindering their deployment in real-time or resource-constrained scenarios. Second，most existing diffusion-based LLIE approaches operate in the spatial domain，which often struggles to recover high-frequency textures and fine structural details faithfully under severe illumination degradation，leading to oversmoothed results or visually disturbing artifacts. Third，the accumulation of prediction errors during reverse diffusion may cause color shifts and temporal inconsistencies across denoising steps，thereby reducing color fidelity and perceptual naturalness.

To address these challenges，this study proposes LLIE with diagonal frequency feature refinement and truncated sampling in conditional diffusion models. The core idea is to exploit jointly the complementary strengths of frequency-domain modeling and accelerated diffusion inference to achieve high-quality enhancement with considerably reduced computational cost. Specifically，instead of performing diffusion directly in the spatial domain，the proposed method conducts forward and reverse diffusion in the low-frequency subspace of the wavelet domain. This design choice is motivated by the observation that global illumination structure and coarse semantic content are predominantly concentrated in low-frequency components. By diffusing only low-frequency coefficients，the proposed framework effectively reduces spatial resolution and computational complexity while preserving the essential structural and illumination information required for faithful enhancement. However，operating in the low-frequency subspace inevitably leads to the loss of high-frequency details，particularly diagonal textures that are critical for visual sharpness. To compensate for this limitation，we introduce a dedicated high-frequency refinement module that explicitly models directional dependencies in the wavelet domain. In contrast to conventional approaches that treat high-frequency bands independently，the proposed module leverages horizontal and vertical high-frequency components to reconstruct missing diagonal information through a dual-path cross-directional attention mechanism. By enabling feature interaction across orthogonal directions，the refinement module effectively alleviates directional sparsity and enhances local texture fidelity，resulting in sharper edges and more natural fine details. To improve efficiency further，an efficient truncated sampling strategy is incorporated into the reverse diffusion process. Instead of executing the full sequence of denoising steps，the proposed approach adopts a step-interrupt mechanism that terminates diffusion at an earlier stage once sufficient reconstruction quality is achieved. This strategy considerably reduces inference time and memory consumption without introducing noticeable degradation in visual quality，making the method more suitable for practical applications. In addition，to mitigate color shifts and enhance stability during reverse diffusion，a contrast-aware correction module is integrated into the denoising stages. This module exploits contrast and color priors accumulated from previous diffusion steps to adjust brightness and chromatic consistency adaptively at each iteration. By explicitly enforcing contrast awareness throughout the sampling process，the proposed method effectively suppresses error accumulation and improves color fidelity in the final enhanced images.

To validate the effectiveness of the proposed method，we conducted extensive experiments on three publicly available paired low-light datasets，comparing the proposed method with several state-of-the-art LLIE methods. Subjective visual comparisons and objective quantitative metrics demonstrate that the proposed method consistently outperforms existing approaches. On the LOLv2-Real dataset，the proposed method achieves improvements over the second-best method by 8.46% in peak signal-to-noise ratio，1.22% in structural similarity index，a 10.19% reduction in learned perceptual image patch similarity（LPIPS），and a 0.38% reduction in naturalness image quality evaluator （NIQE），indicating improved perceptual quality and structural fidelity. To assess the cross-domain generalization ability，we further evaluated the model using the pretrained weights from LOLv2-Real datasets on four unpaired low-light datasets. The proposed method achieved lower NIQE scores than existing methods by 0.82 （LLIE via illumination map estimation），0.39 （multiexposure image fusion） and 0.86 （naturalness photo enhancement），demonstrating strong generalization ability without target-domain fine-tuning. Additionally，ablation studies confirm the contribution of each core module. Compared with spatial-domain diffusion baselines，the proposed method considerably reduced inference time from 7.617 s to 0.473 s per image，and memory usage decreased by 48.53%，highlighting the efficiency and practicality of the proposed design.

The proposed method integrates frequency-domain modeling with an efficient diffusion sampling strategy，introducing three key innovations： 1） performing the diffusion process in the low-frequency subspace of the wavelet domain considerably reduces computational complexity while preserving global structural and illumination information. 2） A dedicated high-frequency refinement module is developed to recover diagonal texture details by leveraging horizontal and vertical high-frequency information，thereby improving the sharpness and perceptual quality of the enhanced images. 3） The integration of an efficient truncated sampling strategy and a contrast-aware correction module improves sampling efficiency and color fidelity，effectively balancing enhancement quality and computational cost. Collectively，these contributions enable the proposed method to achieve superior enhancement performance with substantially reduced inference overhead，demonstrating strong potential for real-world low-light vision applications.