Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset

Lv, Feifan; Li, Yu; Lu, Feng

doi:10.1007/s11263-021-01466-8

Cited by 234 publications

(129 citation statements)

References 74 publications

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“…Zhang et al ( 2020a ) proposed an attention-based image enhancement network that uses attention to suppress chromatic aberrations and noise, which solves the problem of the presence of severe noise in low-light images. Lv et al ( 2019 ) found that the brightness, contrast, and noise regions of an image are closely related to underexposed regions. Thus, they used attention to distinguish between well-lit and poorly-lit regions and they used attention to distinguish between noise and true detail information.…”

Section: Related Workmentioning

confidence: 99%

Low-Light Image Enhancement Network Based on Recursive Network

Liu

Hua

et al. 2022

Front. Neurorobot.

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In low-light environments, image acquisition devices do not obtain sufficient light sources, resulting in low brightness and contrast of images, which poses a great obstacle for other computer vision tasks to be performed. To enable other vision tasks to be performed smoothly, it is essential to enhance the research on low-light image enhancement algorithms. In this article, a multi-scale feature fusion image enhancement network based on recursive structure is proposed. The network uses a dual attention module-Convolutional Block Attention Module. It was abbreviated as CBAM, which includes two attention mechanisms: channel attention and spatial attention. To extract and fuse multi-scale features, we extend the U-Net model using the inception model to form the Multi-scale inception U-Net Module or MIU module for short. The learning of the whole network is divided into T recursive stages, and the input of each stage is the original low-light image and the inter-mediate estimation result of the output of the previous recursion. In the t-th recursion, CBAM is first used to extract channel feature information and spatial feature information to make the network focus more on the low-light region of the image. Next, the MIU module fuses features from three different scales to obtain inter-mediate enhanced image results. Finally, the inter-mediate enhanced image is stitched with the original input image and fed into the t + 1th recursive iteration. The inter-mediate enhancement result provides higher-order feature information, and the original input image provides lower-order feature information. The entire network outputs the enhanced image after several recursive cycles. We conduct experiments on several public datasets and analyze the experimental results subjectively and objectively. The experimental results show that although the structure of the network in this article is simple, the method in this article can recover the details and increase the brightness of the image better and reduce the image degradation compared with other methods.

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Section: Related Workmentioning

confidence: 99%

Low-Light Image Enhancement Network Based on Recursive Network

Liu

Hua

et al. 2022

Front. Neurorobot.

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“…Specifically, LOL (Wei et al, 2018) and SID (C. Chen et al, 2018) consist of pairs of images shot under different exposure time or ISO, while ExDARK (Loh & Chan, 2019) contains images collected from various online platforms. DVS-Dark consists of event images instead of RGB images, which can respond to changes in brightness, and the recent work (Lv, Li, & Lu, 2021) proposed to further extend the scale of the dataset by introducing synthetic low-light images. These research interests have also expanded to the video domain.…”

Section: Dark Visual Datasetsmentioning

confidence: 99%

Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study

Xu¹,

Yang²,

Cao³

et al. 2022

Preprint

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While action recognition (AR) has gained large improvements with the introduction of large-scale video datasets and the development of deep neural networks, AR models robust to challenging environments in real-world scenarios are still under-explored. We focus on the task of action recognition in dark environments, which can be applied to fields such as surveillance and autonomous driving at night. Intuitively, current deep networks along with visual enhancement techniques should be able to handle AR in dark environments, however, it is observed

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“…The illumination features extracted by this module can better guide the enhancement network to enhance the underexposed areas and avoid over-enhancing the normally exposed areas. Inspired by [25], we constrain the output of the network to be between [0, 1], the stronger the light, the lower the output value. According to the illumination smoothness constraint and the sensitivity intensity, the loss function we designed is:…”

Section: Illumination Awareness Networkmentioning

confidence: 99%

Multi-Feature Guided Low-Light Image Enhancement

Hong

Shao

et al. 2021

Applied Sciences

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Due to the characteristics of low signal-to-noise ratio and low contrast, low-light images will have problems such as color distortion, low visibility, and accompanying noise, which will cause the accuracy of the target detection problem to drop or even miss the detection target. However, recalibrating the dataset for this type of image will face problems such as increased cost or reduced model robustness. To solve this kind of problem, we propose a low-light image enhancement model based on deep learning. In this paper, the feature extraction is guided by the illumination map and noise map, and then the neural network is trained to predict the local affine model coefficients in the bilateral space. Through these methods, our network can effectively denoise and enhance images. We have conducted extensive experiments on the LOL datasets, and the results show that, compared with traditional image enhancement algorithms, the model is superior to traditional methods in image quality and speed.

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Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset

Cited by 234 publications

References 74 publications

Low-Light Image Enhancement Network Based on Recursive Network

Low-Light Image Enhancement Network Based on Recursive Network

Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study

Multi-Feature Guided Low-Light Image Enhancement

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