Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Carré, Maxime; Jourlin, Michel

doi:10.3390/s21237906

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The fourth category includes methods based on neural networks, which have made significant advances in image processing in recent years [ 24 , 25 , 26 , 27 ]. Deep learning is the creation of a network model that mimics the human brain’s information processing mechanism.…”

Section: Related Workmentioning

confidence: 99%

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Super-Pixel Guided Low-Light Images Enhancement with Features Restoration

Liu

Yang

Zhong

et al. 2022

Sensors

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Dealing with low-light images is a challenging problem in the image processing field. A mature low-light enhancement technology will not only be conductive to human visual perception but also lay a solid foundation for the subsequent high-level tasks, such as target detection and image classification. In order to balance the visual effect of the image and the contribution of the subsequent task, this paper proposes utilizing shallow Convolutional Neural Networks (CNNs) as the priori image processing to restore the necessary image feature information, which is followed by super-pixel image segmentation to obtain image regions with similar colors and brightness and, finally, the Attentive Neural Processes (ANPs) network to find its local enhancement function on each super-pixel to further restore features and details. Through extensive experiments on the synthesized low-light image and the real low-light image, the experimental results of our algorithm reach 23.402, 0.920, and 2.2490 for Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Natural Image Quality Evaluator (NIQE), respectively. As demonstrated by the experiments on image Scale-Invariant Feature Transform (SIFT) feature detection and subsequent target detection, the results of our approach achieve excellent results in visual effect and image features.

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Section: Related Workmentioning

confidence: 99%

“…It utilizes an efficient learning strategy to gradually restore features to accommodate complex non-linear functions. CNN is been used as the basis of deep learning frameworks in many research works [ 24 , 26 ]. Furthermore, deep learning approaches have been proposed based on the Retinex theory as well [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

Super-Pixel Guided Low-Light Images Enhancement with Features Restoration

Liu

Yang

Zhong

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Increasing the lucidity of sandstorm images using a multistep color reparation algorithm

Al-Ameen

2024

SIViP

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Image Enhancement Thanks to Negative Grey Levels in the Logarithmic Image Processing Framework

Jourlin

2024

Sensors

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The present study deals with image enhancement, which is a very common problem in image processing. This issue has been addressed in multiple works with different methods, most with the sole purpose of improving the perceived quality. Our goal is to propose an approach with a strong physical justification that can model the human visual system. This is why the Logarithmic Image Processing (LIP) framework was chosen. Within this model, initially dedicated to images acquired in transmission, it is possible to introduce the novel concept of negative grey levels, interpreted as light intensifiers. Such an approach permits the extension of the dynamic range of a low-light image to the full grey scale in “real-time”, which means at camera speed. In addition, this method is easily generalizable to colour images and is reversible, i.e., bijective in the mathematical sense, and can be applied to images acquired in reflection thanks to the consistency of the LIP framework with human vision. Various application examples are presented, as well as prospects for extending this work.

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Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising

Cited by 3 publications

References 23 publications

Super-Pixel Guided Low-Light Images Enhancement with Features Restoration

Super-Pixel Guided Low-Light Images Enhancement with Features Restoration

Increasing the lucidity of sandstorm images using a multistep color reparation algorithm

Image Enhancement Thanks to Negative Grey Levels in the Logarithmic Image Processing Framework

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