Subjective low-light image enhancement based on a foreground saliency map model

Hao, Pengcheng; Yang, Meng; Zheng, Nanning

doi:10.1007/s11042-021-11590-9

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…While deep learning-based methods have made progress in low-illumination image enhancement, there are still challenges and limitations that need to be addressed [14,15]. For instance, the enhancement effect of the fully supervised learning method is largely affected by its training data set, so there are some common problems in the enhancement effect.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration via Low-Illumination to Normal-Illumination Networks Based on Retinex Theory

Wen,

Nie,

et al. 2023

Sensors

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Under low-illumination conditions, the quality of the images collected by the sensor is significantly impacted, and the images have visual problems such as noise, artifacts, and brightness reduction. Therefore, this paper proposes an effective network based on Retinex for low-illumination image enhancement. Inspired by Retinex theory, images are decomposed into two parts in the decomposition network, and sent to the sub-network for processing. The reconstruction network constructs global and local residual convolution blocks to denoize the reflection component. The enhancement network uses frequency information, combined with attention mechanism and residual density network to enhance contrast and improve the details of the illumination component. A large number of experiments on public datasets show that our method is superior to existing methods in both quantitative and visual aspects.

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Section: Introductionmentioning

confidence: 99%