Linear Contrast Enhancement Network for Low-Illumination Image Enhancement

Zhou, Zhaorun; Shi, Zhongchao; Ren, Wenqi

doi:10.1109/tim.2022.3232641

Cited by 12 publications

(6 citation statements)

References 61 publications

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“…In order to specifically evaluate the enhanced images, the proposed network was compared subjectively and objectively with other low-illumination image enhancement networks. The networks involved in the comparison are DLN [27], Zero-DEC [28], and LCENet [29], respectively. As shown in figure 12(b), DLN-enhanced images have higher brightness but higher noise and some details are lost.…”

Section: Jinst 18 P07037 5 Discussionmentioning

confidence: 99%

Highly dynamic X-ray image enhancement based on generative adversarial network

Yan¹,

Liu²,

Ding³

et al. 2023

J. Inst.

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When using X-ray to detect industrial workpieces, the images obtained frequently have low contrast, making it difficult to detect defects. This paper proposes a GAN-based X-ray image enhancement network to address this issue. In detail, the X-ray image is concatenated with its antiphase image (as an exposure mask) as the input image, and a trainable Sobel operator is used to extract the edge features of the input image. The input image and edge features are then concatenated and fed into the U-Net generator to be enhanced. The spatial and channel attention models are used to adjust feature weights in U-Net, and a detail extraction network is designed to extract detail features from the input X-ray image. Furthermore, the extracted detail features are fused with the image by the generator after contrast stretching to produce the final enhanced image. Finally, a global-local discriminator is used to discriminate the authenticity of the image so that the contrast of the final obtained image is improved and the details are highlighted. Following experimental validation, the method proposed in this paper has a significant enhancement effect on industrial X-ray images and performs well in terms of enhancing image contrast and highlighting image details.

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Section: Jinst 18 P07037 5 Discussionmentioning

confidence: 99%

Highly dynamic X-ray image enhancement based on generative adversarial network

Yan¹,

Liu²,

Ding³

et al. 2023

J. Inst.

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“…Convolutional neural networks are widely used in areas such as image classification and target detection. In addition, researchers have also applied them to image enhancement [15][16][17][18]. Shen et al [19] combined convolutional neural networks with retinex theory to propose MSR-net for low-light image enhancement.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Zhong,

Fu,

Zhang

2023

Applied Sciences

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Auto-driving detection usually acquires low-light infrared images, which pose a great challenge to the autopilot function at night due to their low contrast and unclear texture details. As a precursor algorithm in the field of automatic driving, the infrared image contrast enhancement method is of great significance in accelerating the operation speed of automatic driving target recognition algorithms and improving the accuracy of object localization. In this study, a convolutional neural network model including feature extraction and image enhancement modules is proposed to enhance infrared images. Specifically, the feature extraction module consists of three branches, a concatenation layer, and a fusion layer that connect in parallel to extract the feature images. The image enhancement module contains eight convolutional layers, one connectivity layer, and one difference layer for enhancing contrast in infrared images. In order to overcome the problem of the lack of a large amount of training data and to improve the accuracy of the model, the brightness and sharpness of the infrared images are randomly transformed to expand the number of pictures in the training set and form more sample pairs. Unlike traditional enhancement methods, the proposed model directly learns the end-to-end mapping between low- and high-contrast images. Extensive experiments from qualitative and quantitative perspectives demonstrate that our method can achieve better clarity in a shorter time.

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“…Deep learning-based methods [9][10][11] for low-light image enhancement typically rely on supervised learning, where a large number of paired low/normal light image samples are required. Unsupervised methods [12,13], on the other hand, use unpaired low/normal light images and employ adversarial training techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The image glare region pixels are processed according to Equation (10), G glare (τ t2 , τ t3 , τ t4 ), the rest of the luminance is adjusted by Equation (11), and the luminance threshold parameters τ t2 , τ t3 , τ t4 are also derived from Equation (4).…”

mentioning

confidence: 99%

“…𝐺 halo (𝜏 , 𝜏 , 𝜏 ), where the luminance threshold parameters 𝜏 , 𝜏 , 𝜏 are also derived from Equation ( 4). 5: The image glare region pixels are processed according to Equation (10), 𝐺 glare (𝜏 , 𝜏 , 𝜏 ), the rest of the luminance is adjusted by Equation ( 11), and the luminance threshold parameters 𝜏 , 𝜏 , 𝜏 are also derived from Equation (4). 6: Output the image after all processing according to Equation (7).…”

mentioning

confidence: 99%

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GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

Li,

Wei,

Pan

et al. 2024

Applied Sciences

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Existing algorithms for enhancing low-light images predominantly focus on the low-light region, which leads to over-enhancement of the glare region, and the high complexity of the algorithm makes it difficult to apply it to embedded devices. In this paper, a GS-AGC algorithm based on regional luminance perception is proposed. The indirect perception of the human eye’s luminance vision was taken into account. All similar luminance pixels that satisfied the luminance region were extracted, and adaptive adjustment processing was performed for the different luminance regions of low-light images. The proposed method was evaluated experimentally on real images, and objective evidence was provided to show that its processing effect surpasses that of other comparable methods. Furthermore, the potential practical value of GS-AGC was highlighted through its effective application in road pedestrian detection and face detection. The algorithm in this paper not only effectively suppressed glare but also achieved the effect of overall image quality enhancement. It can be easily combined with the embedded hardware FPGA for acceleration to improve real-time image processing.

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Linear Contrast Enhancement Network for Low-Illumination Image Enhancement

Cited by 12 publications

References 61 publications

Highly dynamic X-ray image enhancement based on generative adversarial network

Highly dynamic X-ray image enhancement based on generative adversarial network

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception

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