Deep learning-based noise reduction algorithm using patch group technique in cadmium zinc telluride fusion imaging system: A Monte Carlo simulation study

Park, Minji; Lee, Seohyeon; Choi, Serin; Lee, Soeun; Han, Seon-Yeong; Lee, Hye-Jin; Kang, Seong-Hyeon; Lee, Youngjin

doi:10.1016/j.ijleo.2020.164472

Cited by 3 publications

(1 citation statement)

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“…Unlike most CNN denoising models, SSDRN used the batch normalization [92] layer in each block of the algorithm. Reference [93] proposed the patch group deep learning for image denoising. A training set with a patch group was created and then the deep learning method [94,95] was used to reduce the noise.…”

Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%

Methods for image denoising using convolutional neural network: a review

Ilesanmi

2021

Complex Intell. Syst.

193

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Image denoising faces significant challenges, arising from the sources of noise. Specifically, Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in imaging. Convolutional neural network (CNN) has increasingly received attention in image denoising task. Several CNN methods for denoising images have been studied. These methods used different datasets for evaluation. In this paper, we offer an elaborate study on different CNN techniques used in image denoising. Different CNN methods for image denoising were categorized and analyzed. Popular datasets used for evaluating CNN image denoising methods were investigated. Several CNN image denoising papers were selected for review and analysis. Motivations and principles of CNN methods were outlined. Some state-of-the-arts CNN image denoising methods were depicted in graphical forms, while other methods were elaborately explained. We proposed a review of image denoising with CNN. Previous and recent papers on image denoising with CNN were selected. Potential challenges and directions for future research were equally fully explicated.

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Section: Cnn Denoising For Specific Imagesmentioning

confidence: 99%