Results 11 to 20 of about 6,876,175 (322)
Adversarial Gaussian Denoiser for Multiple-Level Image Denoising [PDF]
Sensors, 2021 Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian ...
Aamir Khan +4 more
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PET image denoising based on denoising diffusion probabilistic model [PDF]
European Journal of Nuclear Medicine and Molecular Imaging, 2022 Purpose Due to various physical degradation factors and limited counts received, PET image quality needs further improvements. The denoising diffusion probabilistic model (DDPM) was a distribution learning-based model, which tried to transform a normal ...
Kuang Gong +4 more
semanticscholar +4 more sources
Methods for image denoising using convolutional neural network: a review
Complex & Intelligent Systems, 2021 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.
A. Ilesanmi, Taiwo Ilesanmi
semanticscholar +3 more sources
Medical image denoising using convolutional denoising autoencoders [PDF]
2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 2016 Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances.
Gondara, Lovedeep
core +2 more sources
Boosting of Image Denoising Algorithms [PDF]
SIAM Journal on Imaging Sciences, 2015 In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following "SOS" procedure: (i) (S)trengthen the signal by adding the previous denoised image to the ...
Elad, Michael, Romano, Yaniv
core +3 more sources
Flashlight CNN Image Denoising [PDF]
2020 28th European Signal Processing Conference (EUSIPCO), 2021 This paper proposes a learning-based denoising method called FlashLight CNN (FLCNN) that implements a deep neural network for image denoising. The proposed approach is based on deep residual networks and inception networks and it is able to leverage many more parameters than residual networks alone for denoising grayscale images corrupted by additive ...
Binh, Pham Huu Thanh +2 more
openaire +2 more sources
Image Denoising: The Deep Learning Revolution and Beyond - A Survey Paper - [PDF]
SIAM Journal of Imaging Sciences, 2023 Image denoising (removal of additive white Gaussian noise from an image) is one of the oldest and most studied problems in image processing. An extensive work over several decades has led to thousands of papers on this subject, and to many well ...
Michael Elad +2 more
semanticscholar +1 more source
Overview of Image Denoising Methods
Jisuanji kexue yu tansuo, 2021 In real scenes, due to the imperfections of equipment and systems or the existence of low-light environments, the collected images are noisy. The images will also be affected by additional noise during the compression and transmission process, which will
LIU Liping, QIAO Lele, JIANG Liucheng
doaj +1 more source
A cross Transformer for image denoising [PDF]
Information Fusion, 2023 Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for ...
Chunwei Tian +5 more
semanticscholar +1 more source
Applied Sciences, 2022 Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the ...
Shaoping Xu +5 more
doaj +1 more source