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      저조도 영상의 품질 평가를 위한 지표 제안 = Metrics for Low-Light Image Quality Assessment

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      https://www.riss.kr/link?id=A108732184

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      다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

      In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.
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      In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of ligh...

      In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

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      참고문헌 (Reference)

      1 Zongwei Zhou, "UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation" Institute of Electrical and Electronics Engineers (IEEE) 39 (39): 1856-1867, 2020

      2 H. Huang, "UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation" 1055-1059, 2020

      3 O. Ronneberger, "U-net : Convolutional networks for biomedical image segmentation" 234-241, 2015

      4 Huibin Zhang, "RatUNet: residual U-Net based on attention mechanism for image denoising" PeerJ 8 (8): 234-241, 2022

      5 Wikipedia, "Poisson distribution"

      6 J. Lehtinen, "Noise2Noise: Learning Image Restoration without Clean Data"

      7 D. Bahdanau, "Neural machine translation by jointly learning to align and translate"

      8 H. S. Malvar, "High-quality linear interpolation for demosaicing of Bayer-patterned color images" iii-485, 2004

      9 Imatest, "Electronic Still Picture Camera Resolution Test Chart (ISO-12233) Product Specifications"

      10 R. Gonzalez, "Digital Image Processing" Pearson 358-363, 2018

      1 Zongwei Zhou, "UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation" Institute of Electrical and Electronics Engineers (IEEE) 39 (39): 1856-1867, 2020

      2 H. Huang, "UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation" 1055-1059, 2020

      3 O. Ronneberger, "U-net : Convolutional networks for biomedical image segmentation" 234-241, 2015

      4 Huibin Zhang, "RatUNet: residual U-Net based on attention mechanism for image denoising" PeerJ 8 (8): 234-241, 2022

      5 Wikipedia, "Poisson distribution"

      6 J. Lehtinen, "Noise2Noise: Learning Image Restoration without Clean Data"

      7 D. Bahdanau, "Neural machine translation by jointly learning to align and translate"

      8 H. S. Malvar, "High-quality linear interpolation for demosaicing of Bayer-patterned color images" iii-485, 2004

      9 Imatest, "Electronic Still Picture Camera Resolution Test Chart (ISO-12233) Product Specifications"

      10 R. Gonzalez, "Digital Image Processing" Pearson 358-363, 2018

      11 G. Huang, "Densely Connected Convolutional Networks" 2261-2269, 2017

      12 K. He, "Deep Residual Learning for Image Recognition" 770-778, 2016

      13 Calibrite, "ColorChecker Classic"

      14 G. Buchsbaum, "A spatial processor model for object colour perception" Elsevier BV 310 (310): 1-26, 1980

      15 Javier Gurrola-Ramos, "A Residual Dense U-Net Neural Network for Image Denoising" Institute of Electrical and Electronics Engineers (IEEE) 9 : 31742-31754, 2021

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