Aug 2026· Engineering Research Express· Vol 8· 0 citations· 46 references
Physics
TL;DR
SFH-Net is proposed, an horizontal-vertical-intensity (HVI)-guided luminance-chrominance collaborative enhancement framework that achieves a better trade-off among reconstruction accuracy, structural fidelity, and parameter compactness.
Abstract
Low-light image enhancement remains challenging because brightness amplification often introduces color bias, chromatic noise, and loss of fine details. To address these issues, we propose SFH-Net, an horizontal-vertical-intensity (HVI)-guided luminance-chrominance collaborative enhancement framework. The proposed method operates in the HVI color space, where the H/V chrominance channels and the intensity channel are processed through dedicated branches. In the luminance branch, a frequency-enhanced residual block with a fixed center-square frequency partition provides spectral auxiliary cues for illumination and texture restoration, followed by spatial residual refinement. In the chrominance branch, a U-Net-based chrominance denoiser module predicts signed residual corrections for the H/V channels, suppressing chromatic noise while preserving hue-direction information. During training, a two-stage strategy first stabilizes reconstruction and then introduces adversarial chrominance refinement. Experiments demonstrate that SFH-Net achieves a better trade-off among reconstruction accuracy, structural fidelity, and parameter compactness. The source code is available at: https://github.com/Zhanghuijie-one/SFH-Net.
Image enhancement in low-light conditions is a challenging problem within the field of computer vision, since underexposed images generally lead to poor visibility, low contrast, noise amplification, and color distortion. Recent deep learning approaches have indeed shown promising performance, yet most of them adopt computation-heavy architectures and do not treat the luminance enhancement and color restoration separately; as a result cause unnatural restorations. To this end, in this paper, we present an efficient low-light image enhancement framework based on the Deep White-Balance (DWB) with Dark Channel Prior guidance in the YCbCr color space. The proposed framework separates luminance and chrominance components, making it easier to manage brightness enhancement and color restoration separately. This method aims to generate visually consistent enhanced images while preserving color fidelity and avoiding common enhancement artifacts. We evaluate the performance of the proposed method on reference benchmark datasets (LOL, LOLv2-Synthetic, and LIME) as well as a no-reference benchmark dataset (DICM). The experimental results demonstrate that, although state-of-the-art deep learning methods achieve higher numerical scores, qualitatively, the framework produces enhanced images with consistent contrast and illumination, while retaining color fidelity, all requiring less computational resources. Moreover, it does not require any further training or fine-tuning since the proposed approach is based on a pretrained Deep White Balance model and only uses inference. Experimental results show that the proposed method provides a convincing quality-efficiency compromise for low-light image enhancement.
S. J. Shahbaz, H. G. Daway, Ahlam M. Kadhim· Journal of Intelligent &...· 0 citations
Results indicate that the proposed luminance–chroma collaborative design effectively improves reconstruction fidelity and structural preservation under the evaluated low-light conditions.
Mingxuan Chen, Benxue Sun, Chen Sun et al.· Multimedia Systems· 0 citations
This paper addresses the challenge in low-light image and video enhancement often suffering from over-brightening, color distortion, structure degradation and inter-frame flickering, and presents an enhanced adaptive histogram specification (AHS) method to tackle the problem systematically with a balancing act of adaptive enhancement techniques. Driven by luminance distribution statistics, the AHS method organizes the enhancement process into a unified framework consisting of intensity-adaptive estimation, color preservation, structural rollback, and temporal smoothing. Specifically, (1) enhancement intensity is jointly estimated via the cumulative distribution function (CDF) distance and global luminance deviation, and local luminance correction is introduced to handle non-uniform illumination; (2) in the color space, the original and enhanced chrominance are adaptively fused according to saturation, and difference shrinkage is combined to suppress perceptible color casts; (3) a structural weight is used to perform conservative rollback for edge and texture regions during luminance fusion, reducing over-enhancement artifacts; (4) for video enhancement, temporal smoothing is applied to the enhancement intensity in the parameter domain and combined with a scene response mechanism to suppress flickers caused by frame-wise statistical jitter. Based on a preliminary dataset collected with the same device and a unified evaluation protocol, AHS achieves more balanced luminance preservation, color consistency, and structural fidelity across multiple static scenes, with mean AMBE=8.44, 𝛥𝐸94=3.76 and SSIM=0.884, significantly outperforming traditional methods like global histogram equalization, contrast limited adaptive histogram equalization (CLAHE) and adaptive gamma correction with weighting distribution (AGCWD). For video enhancement, experimental results show that AHS effectively reduces inter-frame fluctuations while maintaining low color difference, achieving a measure of Std |𝛥𝑌|=0.0969, a reduction by 58.86% and 81.62% as compared to CLAHE and AGCWD, respectively. The experimental results demonstrate that AHS can provide a more stable, interpretable, and controllable enhancement of images or videos under cross-scene conditions, offering a reproducible and promising technique for engineering deployment of low-light image and video enhancement.
Yifu Yang, Jianhua Xuan· International Conference on...· 0 citations
SPACE introduces a Depth-Adaptive HVI Transformation to decouple luminance and chrominance under depth guidance, effectively suppressing color-space noise and a Depth-Manifold Modulated Attention mechanism constrains feature interactions within a learned depth manifold, ensuring structural coherence during enhancement.
Yue Zhang, Zhi-Liang Wu, Yuxuan Hou et al.· Proceedings of the Thirty-Fi...· 0 citations
The framework first performs illumination-aware self-supervised denoising to generate a cleaner reference image, which is then used to guide diffusion-based enhancement with a pre-trained backbone, and uses pairwise downsampling together with the proposed illumination prior to suppress noise in dark regions.
This work recast color restoration as an independent sub-problem and decouple it from brightness enhancement, realizing it as a general-purpose post-processing module built on retrieval-augmented generation (RAG).
Li-Wei Lu, S. Miaou· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
