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Relevant bibliographies by topics / Low-light enhancement and denoising

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Journal articles

Academic literature on the topic 'Low-light enhancement and denoising'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Low-light enhancement and denoising"

1

Carré, Maxime, and Michel Jourlin. "Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising." Sensors 21, no. 23 (2021): 7906. http://dx.doi.org/10.3390/s21237906.

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Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the abi

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2

Zhang, Jialiang, Ruiwen Ji, Jingwen Wang, Hongcheng Sun, and Mingye Ju. "DEGAN: Decompose-Enhance-GAN Network for Simultaneous Low-Light Image Lightening and Denoising." Electronics 12, no. 14 (2023): 3038. http://dx.doi.org/10.3390/electronics12143038.

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Images taken in low-light situations frequently have a significant quality reduction. Taking care of these degradation problems in low-light images is essential for raising their visual quality and enhancing high-level visual task performance. However, because of the inherent information loss in dark images, conventional Retinex-based approaches for low-light image enhancement frequently fail to accomplish real denoising. This research introduces DEGANet, a revolutionary deep-learning framework created particularly for improving and denoising low-light images. To overcome these restrictions, D

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3

Kim, Minjae, Dubok Park, David Han, and Hanseok Ko. "A novel approach for denoising and enhancement of extremely low-light video." IEEE Transactions on Consumer Electronics 61, no. 1 (2015): 72–80. http://dx.doi.org/10.1109/tce.2015.7064113.

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4

Malik, Sameer, and Rajiv Soundararajan. "A low light natural image statistical model for joint contrast enhancement and denoising." Signal Processing: Image Communication 99 (November 2021): 116433. http://dx.doi.org/10.1016/j.image.2021.116433.

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5

Das Mou, Trisha, Saadia Binte Alam, Md Hasibur Rahman, Gautam Srivastava, Mahady Hasan, and Mohammad Faisal Uddin. "Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation." Applied Sciences 13, no. 2 (2023): 1034. http://dx.doi.org/10.3390/app13021034.

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Images under low-light conditions suffer from noise, blurring, and low contrast, thus limiting the precise detection of objects. For this purpose, a novel method is introduced based on convolutional neural network (CNN) dual attention unit (DAU) and selective kernel feature synthesis (SKFS) that merges with the Retinex theory-based model for the enhancement of dark images under low-light conditions. The model mentioned in this paper is a multi-scale residual block made up of several essential components equivalent to an onward convolutional neural network with a VGG16 architecture and various

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6

Han, Guang, Yingfan Wang, Jixin Liu, and Fanyu Zeng. "Low-light images enhancement and denoising network based on unsupervised learning multi-stream feature modeling." Journal of Visual Communication and Image Representation 96 (October 2023): 103932. http://dx.doi.org/10.1016/j.jvcir.2023.103932.

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7

Hu, Linshu, Mengjiao Qin, Feng Zhang, Zhenhong Du, and Renyi Liu. "RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images." Remote Sensing 13, no. 1 (2020): 62. http://dx.doi.org/10.3390/rs13010062.

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Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-

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8

Zhao, Meng Ling, and Min Xia Jiang. "Research on Enhanced of Mine-Underground Picture." Advanced Materials Research 490-495 (March 2012): 548–52. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.548.

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Because of the based on S3C6410 Field information recorder mine- underground non-uniform illumination and mine- underground non-uniform illumination that a large of noise collected and transferred,image is low contrast ,dim and dark. Based on the theory of Donoho's wavelet threshold denoising, several typical wavelet threshold denoising methods are compared.the best denoising effect of peak signal to noise ratio is obtained. The image enhancement method that combination of the adaptive thresholding denoising and histogram equalization is proposed. The experiment result shows that the method ha

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9

Wu, Zeju, Yang Ji, Lijun Song, and Jianyuan Sun. "Underwater Image Enhancement Based on Color Correction and Detail Enhancement." Journal of Marine Science and Engineering 10, no. 10 (2022): 1513. http://dx.doi.org/10.3390/jmse10101513.

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To solve the problems of underwater image color deviation, low contrast, and blurred details, an algorithm based on color correction and detail enhancement is proposed. First, the improved nonlocal means denoising algorithm is used to denoise the underwater image. The combination of Gaussian weighted spatial distance and Gaussian weighted Euclidean distance is used as the index of nonlocal means denoising algorithm to measure the similarity of structural blocks. The improved algorithm can retain more edge features and texture information while maintaining noise reduction ability. Then, the imp

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10

Xu, Xiaogang, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. "Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3054–62. http://dx.doi.org/10.1609/aaai.v37i3.25409.

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Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR effi

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