Enhanced adaptive histogram specification with multi-metric balancing for low-light image and video enhancement

Abstract

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.