LLCNN: A convolutional neural network for low-light image enhancement

Li, Tao; Zhu, Chuang; Xiang, Guoqing; Li, Yuan; Jia, Huizhu; Xie, Xiaodong

doi:10.1109/vcip.2017.8305143

Cited by 141 publications

(78 citation statements)

References 15 publications

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“…CNNs have been used as the basis of deep learning frameworks in many research works [256]- [259]. Tao et al proposed a low-light CNN (LLCNN) in which a multistage characteristic map was used to generate an enhanced image by learning from low-light images with different nuclei [260]. In [261], a global illumination-aware and detail-preserving network (GLADNet) was designed.…”

Section: G Methods Based On Machine Learningmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Section: G Methods Based On Machine Learningmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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“…In [48], a deep network framework, low-light convolutional neural network (LLCNN), was designed to perform enhancement for input low-light images and improve the effect of LLNet. This network was constructed based on the structure of a residual convolutional neural network.…”

Section: Deep Learning Methods For Low-light Image Enhancementmentioning

confidence: 99%

“…To generate the training data, we modify the clear source images to generate low-light image samples as done in LLCNN [48]. First, the values of image pixels are normalized to [0,1] as the pre-processing strategy.…”

Section: Network Training and Training Data Generationmentioning

confidence: 99%

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Deep Refinement Network for Natural Low-Light Image Enhancement in Symmetric Pathways

Jiang

Jing

et al. 2018

Symmetry

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Due to the cost limitation of camera sensors, images captured in low-light environments often suffer from low contrast and multiple types of noise. A number of algorithms have been proposed to improve contrast and suppress noise in the input low-light images. In this paper, a deep refinement network, LL-RefineNet, is built to learn from the synthetical dark and noisy training images, and perform image enhancement for natural low-light images in symmetric—forward and backward—pathways. The proposed network utilizes all the useful information from the down-sampling path to produce the high-resolution enhancement result, where global features captured from deeper layers are gradually refined using local features generated by earlier convolutions. We further design the training loss for mixed noise reduction. The experimental results show that the proposed LL-RefineNet outperforms the comparative methods both qualitatively and quantitatively with fast processing speed on both synthetic and natural low-light image datasets.

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“…However, these techniques do not take the imprecision of gray values into account. Thereby some filtering-based methods (Yang et al 2003;Karumuri and Kumari 2017;Bhadu et al 2017) and neural network-based methods (Tao et al 2017;Park et al 2018;Ma et al 2007;Zhang et al 2010;Xu et al 2014) are developed.…”

Section: Introductionmentioning

confidence: 99%

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

Nie

Cao

et al. 2018

J Ambient Intell Human Comput

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With the rapid growth of medical big data, medical signal processing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound, MRI machines. Hence, based on pulse coupled neural network (PCNN) and the classical visual receptive field (CVRF) with the difference of two Gaussians (DOG), a contrast enhancement of MRI image is suggested to improve the accuracy of clinical diagnosis for smarter mobile healthcare. As one premise, the parameters of DOG are estimated from the fundamentals of CVRF; then the PCNN parameters in image enhancement are estimated eventually with the help of DOG. As a result, the MRI images can be enhanced adaptively. Due to the exponential decay of the dynamic threshold and the pulses coupling among neurons, PCNN effectively enhances the contrast of low grey levels in MRI image. Moreover, because of the inhibitory effects from inhibitory region in CVRF, PCNN also effectively preserves the structures such as edges for enhanced results. Experiments on several MRI images show that the proposed method performs better than other methods by improving contrast and preserving structures well.

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LLCNN: A convolutional neural network for low-light image enhancement

Cited by 141 publications

References 15 publications

An Experiment-Based Review of Low-Light Image Enhancement Methods

An Experiment-Based Review of Low-Light Image Enhancement Methods

Deep Refinement Network for Natural Low-Light Image Enhancement in Symmetric Pathways

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

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