Unpaired Night-To-Day Translation: Image Restoration And Style Transfer Under Low Illumination

Li, Haoling; Chen, Yuanyuan

doi:10.1109/icip42928.2021.9506248

Cited by 4 publications

(1 citation statement)

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“…It also adopts a global discriminator to improve the overall reconstruction quality and restore brightness. Li and Chen [ 34 ] proposed to combine DCGAN (deep convolutional generative adversarial network) and MSRCP (multiscale Retinex with chromaticity preservation) algorithm to determine the mapping from night to day.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Retinex-Based Illumination Attention Low-Light Enhancement Network for Autonomous Driving at Night

Zhao

Yong

Jian

2022

Computational Intelligence and Neuroscience

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Low-light image enhancement is a preprocessing work for many recognition and tracking tasks for autonomous driving at night. It needs to handle various factors simultaneously including uneven lighting, low contrast, and artifacts. We propose a novel end-to-end Retinex-based illumination attention low-light enhancement network. Specifically, our proposed method adopts multibranch architecture to extract rich features for different depth levels. Meanwhile, we consider the features from different scales in built-in illumination attention module. We encode reflectance features and illumination features into latent space based on Retinex in each submodule, which could cater for highly ill-posed image decomposition tasks. It aims to enhance the desired illumination features under different receptive fields. Subsequently, we propose a memory gate mechanism to learn adaptively long-term and short-term memory. Their weight could control how many high-level and low-level features should be reserved. This method could improve the image quality from both different feature scales and feature levels. Comprehensive experiments on BDD10K and cityscapes datasets demonstrate that our proposed method outperforms various types of methods in terms of visual quality and quantitative metrics. We also show that our proposed method has certain antinoise capability and generalizes well without fine-tuning when dealing with unseen images. Meanwhile, our restoration performance is comparable to that of advanced computationally intensive models.1

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Section: Related Workmentioning

confidence: 99%