Results 11 to 20 of about 49,854 (268)
Hangkong gongcheng jinzhan, 2023 The effect of point cloud denoising is very important to the subsequent surface fitting and modeling design in 3D scanning process. How to extract feature points quickly and accurately has become a research hotspot.However,the key point of point cloud ...
LI Binpeng, MAO Jian, YANG Jie, CAI Hang
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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
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Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
Applied Sciences, 2023 In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition.
Roopdeep Kaur +2 more
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IEEE Access, 2022 Traditional denoising methods for seismic exploration data design a corresponding mathematical denoising model batch according to the different properties of different random noises, which is a tedious and time-consuming process.
Liang Guo +5 more
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Auto-Denoising for EEG Signals Using Generative Adversarial Network
Sensors, 2022 The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel
Yang An, Hak Keung Lam, Sai Ho Ling
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Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm
Technologies, 2023 Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details.
Rusul Sabah Jebur +4 more
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Denoising an Image by Denoising Its Curvature Image [PDF]
SIAM Journal on Imaging Sciences, 2014 The first author acknowledges partial support by European Research Council, Starting Grant ref. 306337, and/nby Spanish grants AACC, ref. TIN2011-15954-E, and Plan Nacional, ref. TIN2012-38112. The second author was supported in part by NSF-DMS #0915219.
Marcelo Bertalmío, Stacey Levine
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Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement
IEEE Access, 2020 This paper proposes an image denoising training framework based on Wasserstein Generative Adversarial Networks (WGAN) and applies it to cell image denoising. Cell image denoising is a challenging task which has high requirement on the recovery of feature
Songkui Chen +3 more
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GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks [PDF]
ACM Transactions on Graphics, 2022 In this article, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks ( GCNs ). Unlike previous learning-based mesh denoising methods that exploit handcrafted or voxel-based representations for feature learning, our method explores ...
Yuefan Shen +7 more
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Hyperspectral Imagery Denoising Using Minimum Noise Fraction and Video Non-Local Bayes Algorithms
Canadian Journal of Remote Sensing, 2022 Hyperspectral imagery (HSI) denoising is a popular research topic in remote sensing. In this paper, we propose a novel method for HSI denoising by performing Minimum Noise Fraction (MNF) to the original HSI data cube, thresholding the noisy output bands ...
Guang Yi Chen +2 more
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