Toward Fast, Flexible, and Robust Low-Light Image Enhancement

Ma, Lei; Ma, Tengyu; Liu, Risheng; Fan, Xiaohui; Luo, Zhongxuan

doi:10.48550/arxiv.2204.10137

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…This paper presents the illumination estimation loss function consisting of color constancy loss function L col , ex-posure control loss function L exp , illumination smoothness Loss function L tv and fidelity Loss function L f id [39] to constrain the training process.…”

Section: Illumination Estimation Loss Functionmentioning

confidence: 99%

A Night Image Enhancement Algorithm Based on MDIFE-Net Curve Estimation

Zhang

Dan

et al. 2023

IEICE Trans. Inf. & Syst.

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In order to solve the low-quality problems such as low brightness, poor contrast, noise interference and color imbalance in night images, a night image enhancement algorithm based on MDIFE-Net curve estimation is presented. This algorithm mainly consists of three parts: Firstly, we design an illumination estimation curve (IEC), which adjusts the pixel level of the low illumination image domain through a non-linear fitting function, maps to the enhanced image domain, and effectively eliminates the effect of illumination loss; Secondly, the DCE-Net is improved, replacing the original Relu activation function with a smoother Mish activation function, so that the parameters can be better updated; Finally, illumination estimation loss function, which combines image attributes with fidelity, is designed to drive the no-reference image enhancement, which preserves more image details while enhancing the night image. The experimental results show that our method can not only effectively improve the image contrast, but also make the details of the target more prominent, improve the visual quality of the image, and make the image achieve a better visual effect. Compared with four existing low illumination image enhancement algorithms, the NIQE and STD evaluation index values are better than other representative algorithms, verify the feasibility and validity of the algorithm, and verify the rationality and necessity of each component design through ablation experiments.

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Section: Illumination Estimation Loss Functionmentioning

confidence: 99%