Results 1 to 10 of about 18,244,520 (347)
Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper [PDF]
SIAM Journal on 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
exaly +2 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
exaly +2 more sources
Overview of Research on Digital Image Denoising Methods [PDF]
SensorsDuring image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like ...
Jing Mao +3 more
doaj +2 more sources
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
doaj +3 more sources
Self-Supervised Joint Learning for pCLE Image Denoising [PDF]
SensorsProbe-based confocal laser endoscopy (pCLE) has emerged as a powerful tool for disease diagnosis, yet it faces challenges such as the formation of hexagonal patterns in images due to the inherent characteristics of fiber bundles.
Kun Yang +4 more
doaj +2 more sources
Efficient real-world image denoising using multi-scale gaussian pyramids [PDF]
Scientific ReportsThe field of image denoising has undergone significant advancements over the years. Recently, Convolutional Neural Networks (CNN) based denoising methods have shown remarkable performance in image denoising.
Asha Rani, Rosepreet Kaur Bhogal
doaj +2 more sources
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion [PDF]
IEEE International Conference on Computer Vision, 2023 Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and texture details.
Zixiang Zhao +9 more
semanticscholar +1 more source
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data [PDF]
Computer Vision and Pattern Recognition, 2023 Recently, self-supervised neural networks have shown excellent image denoising performance. How-ever, current dataset free methods are either computationally expensive, require a noise model, or have inad-equate image quality. In this work we show that a
Y. Mansour, Reinhard Heckel
semanticscholar +1 more source
Denoising Diffusion Models for Plug-and-Play Image Restoration [PDF]
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2023 Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior.
Yuanzhi Zhu +6 more
semanticscholar +1 more source
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model [PDF]
International Conference on Learning Representations, 2022 Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR ...
Yinhuai Wang, Jiwen Yu, Jian Zhang
semanticscholar +1 more source