DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations

Tang, Linfeng; Ma, Jiayi; Zhang, Hao; Guo, Xiaojie

doi:10.1109/tnnls.2022.3190880

Cited by 10 publications

(3 citation statements)

References 47 publications

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“…Additionally, we employed a non-reference image quality assessment method (NIQE) [27], commonly used in the field of image enhancement at this stage, to evaluate the image quality. The state-of-the-art methods of RetinexNet [12], GladNet [13], KinD [18], Zero-DCE [33], LLFlow [34], DRLIE [35], and URetinex [36] are involved as the competitors. All algorithms were run in the same environment, as shown in Table 4.…”

Section: Experimental Setup and Model Trainingmentioning

confidence: 99%

TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network

Mu,

Ma,

Wang

et al. 2024

Electronics

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Retinex-based deep learning methods show good low-light enhancement performance and are mainstream approaches in this field. However, the current methods for enhancing low-light images are insufficient in accurately separating illumination and comprehensively restoring degraded information, especially in images with uneven or extremely low illumination levels. This situation often leads to the over-enhancement of bright regions, a loss of detail, and color distortion in the final images. To address these issues, we improved three subnetworks in the classic KinD network, and proposed a trans-scale and refined low-light image enhancement network. Compared with KinD, our method shows more precise image decomposition performance, enhancing the expressiveness of the reflection and illumination components in order to better depict image details, colors, and lighting information. For reflectance restoration, we use a U-shaped network for cross-scale denoising, incorporating attention mechanisms and a color saturation loss to restore image textures and colors. For light adjustment, we apply fine-grained light adjustment approaches to simultaneously enhance brightness in dark areas and prevent excessive enhancement in bright areas. The experimental results demonstrate that with the LOL dataset, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of TSRNet are improved by 2–31% and 5–34%, respectively, when compared with the mainstream methods.

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Section: Experimental Setup and Model Trainingmentioning

confidence: 99%

TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network

Mu,

Ma,

Wang

et al. 2024

Electronics

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show abstract

“…The lack of illumination leads to the loss of image information, which severely affects the execution of visual tasks, e.g., face recognition, object detection, dataset preparation, and autonomous driving ( Li J. et al, 2021 ; Liu et al, 2021 ; Tang et al, 2022 ; Guo et al, 2023 ). Capturing images in low-light conditions poses a challenge owing to the limited aperture size, demand for instantaneous processing, and limited memory resources.…”

Section: Introductionmentioning

confidence: 99%

DCENet-based low-light image enhancement improved by spiking encoding and convLSTM

Wang,

Zhang

et al. 2024

Front. Neurosci.

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The direct utilization of low-light images hinders downstream visual tasks. Traditional low-light image enhancement (LLIE) methods, such as Retinex-based networks, require image pairs. A spiking-coding methodology called intensity-to-latency has been used to gradually acquire the structural characteristics of an image. convLSTM has been used to connect the features. This study introduces a simplified DCENet to achieve unsupervised LLIE as well as the spiking coding mode of a spiking neural network. It also applies the comprehensive coding features of convLSTM to improve the subjective and objective effects of LLIE. In the ablation experiment for the proposed structure, the convLSTM structure was replaced by a convolutional neural network, and the classical CBAM attention was introduced for comparison. Five objective evaluation metrics were compared with nine LLIE methods that currently exhibit strong comprehensive performance, with PSNR, SSIM, MSE, UQI, and VIFP exceeding the second place at 4.4% (0.8%), 3.9% (17.2%), 0% (15%), 0.1% (0.2%), and 4.3% (0.9%) on the LOL and SCIE datasets. Further experiments of the user study in five non-reference datasets were conducted to subjectively evaluate the effects depicted in the images. These experiments verified the remarkable performance of the proposed method.

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“…The lack of illumination leads to the loss of image information, which severely affects the execution of visual tasks, e.g., face recognition, object detection, dataset preparation, and autonomous driving Tang et al, 2022;Guo et al, 2023). Capturing images in low-light conditions poses a challenge owing to the limited aperture size, demand for instantaneous processing, and limited memory resources.…”

Section: Introductionmentioning

confidence: 99%

Advanced Methods and Applications for Neurointelligence

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations

Cited by 10 publications

References 47 publications

TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network

TSRNet: A Trans-Scale and Refined Low-Light Image Enhancement Network

DCENet-based low-light image enhancement improved by spiking encoding and convLSTM

Advanced Methods and Applications for Neurointelligence

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