Principle-Inspired Multi-Scale Aggregation Network for Extremely Low-Light Image Enhancement

Zhang, Jiaao; Liu, Risheng; Ma, Long; Zhong, Wei; Fan, Xin; Luo, Zhongxuan

doi:10.1109/icassp40776.2020.9053261

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…To demonstrate the efficiency of our method, we evaluate it qualitative and quantitative on MIT-Adobe5k and LOL and compare our method with seven state-of-the-art CNN-based methods (RetinexNet [4], KinD [5], EnGAN [25], MIRNet [26], PMANet [27], DeepUPE [17], DRBN [18]). For fair comparison, The results are reproduced by publicly-available models released by the authors.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Hardware-Aware Low-Light Image Enhancement via One-Shot Neural Architecture Search with Shrinkage Sampling

Yao

Liu

Zhang

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Low-light image enhancement has traditionally been tackled by training a heuristically designed neural network architecture. Despite the success of these approaches, the heuristic design pattern inherently not only hinders further optimization of network architectures, but also limits the factors that the designer can take into consideration. As a result, these methods are difficult to achieve a balance between enhancing performance and hardware related performance. In this paper, we equip a basic enhancing algorithm with a neural architecture search technique. This technique helps to automatically search an optimal hardware-aware architecture while also increases neglectable computation burden. In this work, we propose a shrinkage sampling strategy to drastically decrease the computation cost of neural architecture search while improving the quality of search. Extensive experiments on various benchmarks demonstrate that our algorithm achieves state-of-the-art performance with higher speed.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

Hardware-Aware Low-Light Image Enhancement via One-Shot Neural Architecture Search with Shrinkage Sampling

Yao

Liu

Zhang

et al. 2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

Self Cite

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“…Method [10,11] does not focus on extremely low light image enhancement. Zhang et al [12] develop a principle-inspired multi-scale aggregation network to achieve the exposure enhancement and noises removal on extremely low-light conditions, but the effects are not very well.…”

Section: Low-light Image Enhancementmentioning

confidence: 99%

PD-GAN: Perceptual-Details GAN for Extremely Noisy Low Light Image Enhancement

Liu

Wang

Zeng

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Extremely noisy low light enhancement suffers from highlevel noise, loss of texture detail, and color degradation. When recovering color or illumination for images taken in a dark environment, the challenge for networks is how to balance the enhancement for noise and texture details for a good visual effect. A single network is not suitable for solving the ill-posed problem of mapping the input image's noise to the clear target in the ground truth. To solve the problems, we pro-pose perceptual-details GAN (PD-GAN) utilizing Zero-DCE to initially recover illumination and combine residual dense-block Encoder-Decoder structure to suppress noise while finely adjusting the illumination. Besides, fractional differential gradient masks are integrated into the discriminator to enhance details. Experiment results demonstrate that PD-GAN outperforms other methods on the extremely low-light image dataset.

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