FLA-Net: multi-stage modular network for low-light image enhancement

Yu, Ning; Li, Jinjiang; Hua, Zhen

doi:10.1007/s00371-022-02402-8

Cited by 10 publications

(4 citation statements)

References 51 publications

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“…This method addresses the issue of insufficient enhancement under various lighting conditions by introducing an illumination adaptive-enhancement network. To improve the image enhancement effect, the literature [ 25 ] proposed a multi-stage modular network (FLA-Net) that focuses more on the texture information of the image via the LBP module. The literature [ 26 ] proposed a structural texture-aware network that solves the color distortion problem using a color-loss function.…”

Section: Related Workmentioning

confidence: 99%

Cyclic Generative Attention-Adversarial Network for Low-Light Image Enhancement

Zhen,

Peng,

2023

Sensors

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Images captured under complex conditions frequently have low quality, and image performance obtained under low-light conditions is poor and does not satisfy subsequent engineering processing. The goal of low-light image enhancement is to restore low-light images to normal illumination levels. Although many methods have emerged in this field, they are inadequate for dealing with noise, color deviation, and exposure issues. To address these issues, we present CGAAN, a new unsupervised generative adversarial network that combines a new attention module and a new normalization function based on cycle generative adversarial networks and employs a global–local discriminator trained with unpaired low-light and normal-light images and stylized region loss. Our attention generates feature maps via global and average pooling, and the weights of different feature maps are calculated by multiplying learnable parameters and feature maps in the appropriate order. These weights indicate the significance of corresponding features. Specifically, our attention is a feature map attention mechanism that improves the network’s feature-extraction ability by distinguishing the normal light domain from the low-light domain to obtain an attention map to solve the color bias and exposure problems. The style region loss guides the network to more effectively eliminate the effects of noise. The new normalization function we present preserves more semantic information while normalizing the image, which can guide the model to recover more details and improve image quality even further. The experimental results demonstrate that the proposed method can produce good results that are useful for practical applications.

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Section: Related Workmentioning

confidence: 99%

Cyclic Generative Attention-Adversarial Network for Low-Light Image Enhancement

Zhen,

Peng,

2023

Sensors

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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).…”

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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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Two-stage image decomposition and color regulator for low-light image enhancement

2022

Vis Comput

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FLA-Net: multi-stage modular network for low-light image enhancement

Cited by 10 publications

References 51 publications

Cyclic Generative Attention-Adversarial Network for Low-Light Image Enhancement

Cyclic Generative Attention-Adversarial Network for Low-Light Image Enhancement

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

Two-stage image decomposition and color regulator for low-light image enhancement

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