Semantically-guided low-light image enhancement

Xie, Junyi; Bian, Hao; Wu, Yuanfeng; Shan, Linmin; Hao, Shijie

doi:10.1016/j.patrec.2020.07.041

Cited by 27 publications

(8 citation statements)

References 17 publications

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“…The network training and testing experiments are completed on the NVIDIA GTX 2080 GPU, and the implementation code is based on the TensorFlow framework. In order to verify the performance and effect of the proposed algorithm in this paper, the following algorithms are used: SEM (Xie et al, 2020 ), VBSA (Kim et al, 2020 ), APM (Feng et al, 2020 ).…”

Section: Experiments and Analysismentioning

confidence: 99%

“…As can be seen from the results of the LOL data set in Table 2 , in terms of PSNR index, the proposed algorithm is generally superior to other advanced algorithms. According to the studies in Xie et al ( 2020 ), Kim et al ( 2020 ), and Feng et al ( 2020 ), PSNR index is widely used in image evaluation because it is easy to calculate. However, its calculation is based on error sensitivity, and it often appears inconsistent with human perception system in the evaluation.…”

Section: Experiments and Analysismentioning

confidence: 99%

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A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

Zhang

et al. 2021

Front. Neurorobot.

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With the development of computer vision, high quality images with rich information have great research potential in both daily life and scientific research. However, due to different lighting conditions, surrounding noise and other reasons, the image quality is different, which seriously affects people's discrimination of the information in the image, thus causing unnecessary conflicts and results. Especially in the dark, the images captured by the camera are difficult to identify, and the smart system relies heavily on high-quality input images. The image collected in low-light environment has the characteristic with high noise and color distortion, which makes it difficult to utilize the image and can not fully explore the rich value information of the image. In order to improve the quality of low-light image, this paper proposes a Heterogenous low-light image enhancement method based on DenseNet generative adversarial network. Firstly, the generative network of generative adversarial network is realized by using DenseNet framework. Secondly, the feature map from low light image to normal light image is learned by using the generative adversarial network. Thirdly, the enhancement of low-light image is realized. The experimental results show that, in terms of PSNR, SSIM, NIQE, UQI, NQE and PIQE indexes, compared with the state-of-the-art enhancement algorithms, the values are ideal, the proposed method can improve the image brightness more effectively and reduce the noise of enhanced image.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

Zhang

et al. 2021

Front. Neurorobot.

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show abstract

“…Employing such consistency can help to avoid local uneven exposure. Although methods (Fan et al 2020;Xie et al 2020) apply semantic information as guidance to improve image enhancement, they ignore the consistency between pixels of the same semantic category.…”

Section: Introductionmentioning

confidence: 99%

Semantically Contrastive Learning for Low-Light Image Enhancement

Dong

Wei

et al. 2022

AAAI

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Low-light image enhancement (LLE) remains challenging due to the unfavorable prevailing low-contrast and weak-visibility problems of single RGB images. In this paper, we respond to the intriguing learning-related question -- if leveraging both accessible unpaired over/underexposed images and high-level semantic guidance, can improve the performance of cutting-edge LLE models? Here, we propose an effective semantically contrastive learning paradigm for LLE (namely SCL-LLE). Beyond the existing LLE wisdom, it casts the image enhancement task as multi-task joint learning, where LLE is converted into three constraints of contrastive learning, semantic brightness consistency, and feature preservation for simultaneously ensuring the exposure, texture, and color consistency. SCL-LLE allows the LLE model to learn from unpaired positives (normal-light)/negatives (over/underexposed), and enables it to interact with the scene semantics to regularize the image enhancement network, yet the interaction of high-level semantic knowledge and the low-level signal prior is seldom investigated in previous methods. Training on readily available open data, extensive experiments demonstrate that our method surpasses the state-of-the-arts LLE models over six independent cross-scenes datasets. Moreover, SCL-LLE's potential to benefit the downstream semantic segmentation under extremely dark conditions is discussed. Source Code: https://github.com/LingLIx/SCL-LLE.

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“…Intensifying the image directly is the simplest and the inherent approach to bringing the low-light regions into the light. But, amplifying the low light regions paves the way to other problems like enhancing the naturally bright regions to get saturated and lose intricate details ( Ke et al, 2020 ; Xie et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Low-Light Image Enhancement Based on Generative Adversarial Network

Abirami

Vincent

2021

Front. Genet.

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Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.

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Semantically-guided low-light image enhancement

Cited by 27 publications

References 17 publications

A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

Semantically Contrastive Learning for Low-Light Image Enhancement

Low-Light Image Enhancement Based on Generative Adversarial Network

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