Results 1 to 10 of about 6,876,175 (322)
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
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
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
Image denoising method integrating ridgelet transform and improved wavelet threshold. [PDF]
PLoS ONEIn the field of image processing, common noise types include Gaussian noise, salt and pepper noise, speckle noise, uniform noise and pulse noise. Different types of noise require different denoising algorithms and techniques to maintain image quality and
Bingbing Li, Yao Cong, Hongwei Mo
doaj +2 more sources
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 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
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 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
Multi-stage image denoising with the wavelet transform [PDF]
Pattern Recognition, 2022 Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging depth of designed networks to obtain better denoising performance, which ...
Chunwei Tian +5 more
semanticscholar +1 more source
Dual Residual Attention Network for Image Denoising [PDF]
Pattern Recognition, 2023 In image denoising, deep convolutional neural networks (CNNs) can obtain favorable performance on removing spatially invariant noise. However, many of these networks cannot perform well on removing the real noise (i.e.
Wencong Wu +4 more
semanticscholar +1 more source