What does this research mean for the field?

An efficient low-light image enhancement framework combining Deep White-Balance and Dark Channel Prior in the YCbCr color space produces visually natural restorations with consistent contrast and color fidelity while requiring fewer computational resources than state-of-the-art deep learning methods. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Develop an efficient low-light image restoration framework that separates luminance enhancement and color correction to prevent unnatural visual artifacts without requiring model retraining.

September 2, 2026Journal of Intelligent & Fuzzy Systems

Deep white balance and dark channel prior fusion: Toward efficient and natural low-light image restoration

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Authors

SSShahad J. ShahbazMustansiriyah University HDHazim G. DawayMustansiriyah University AKAhlam M. KadhimMustansiriyah University

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Implication

Computational study demonstrates natural illumination and color fidelity in underexposed images using fused white balance and dark channel priors, highlighting an efficient training-free solution.

Key Points

Develop an efficient low-light image restoration framework that separates luminance enhancement and color correction to prevent unnatural visual artifacts without requiring model retraining.
Fused a pretrained Deep White-Balance (DWB) model with Dark Channel Prior (DCP) guidance operated in the YCbCr color space to decouple luminance from chrominance.
Assessed performance in an inference-only setting without additional training across reference datasets (LOL, LOLv2-Synthetic, LIME) and a no-reference dataset (DICM).
Produced enhanced images with qualitatively superior contrast consistency, illumination balance, and color fidelity compared to heavy deep learning baselines, avoiding typical enhancement artifacts.
Achieved a favorable quality-efficiency trade-off by minimizing computational resource demands via a direct, inference-only pipeline.

Cite This Study

Shahbaz et al. (2026) studied this question.

synapsesocial.com/papers/6a97e28dc562ede874ec6a6c https://doi.org/10.1177/18758967261481557

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Implication

Computational study demonstrates natural illumination and color fidelity in underexposed images using fused white balance and dark channel priors, highlighting an efficient training-free solution.

Deep white balance and dark channel prior fusion: Toward efficient and natural low-light image restoration

Discussion

Member takes

Implication

Key Points

Cite This Study

Deep white balance and dark channel prior fusion: Toward efficient and natural low-light image restoration

Discussion

Member takes

Implication

Key Points

Cite This Study

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