Results 71 to 80 of about 259,715 (355)
Dilated Deep Residual Network for Image Denoising [PDF]
, 2017 Variations of deep neural networks such as convolutional neural network (CNN) have been successfully applied to image denoising. The goal is to automatically learn a mapping from a noisy image to a clean image given training data consisting of pairs of ...
Hu, Kaoning +2 more
core +3 more sources
Topaz-Denoise: general deep denoising models for cryoEM and cryoET [PDF]
Nature Communications, 2019 AbstractCryo-electron microscopy (cryoEM) is becoming the preferred method for resolving protein structures. Low signal-to-noise (SNR) in cryoEM images reduces the confidence and throughput of structure determination during several steps of data processing, resulting in impediments such as missing particle orientations.
Tristan Bepler +3 more
openaire +6 more sources
Fuzzy rule based multiwavelet ECG signal denoising [PDF]
, 2008 Since different multiwavelets, pre- and post-filters have different impulse responses and frequency responses, different multiwavelets, pre- and post-filters should be selected and applied at different noise levels for signal denoising if signals are ...
Chan, Yick-Po +5 more
core +1 more source
A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
Applied Sciences, 2020 In this paper, we propose a preprocessing strategy for denoising of speech data based on speech segment detection. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets. Furthermore, it
Seung-Jun Lee, Hyuk-Yoon Kwon
doaj +1 more source
Denoising an Image by Denoising Its Components in a Moving Frame [PDF]
, 2014 This work was supported by European Research Council, Starting Grant ref. 306337, and by Spanish grants AACC, ref. TIN2011-15954-E, and Plan Na- cional, ref. TIN2012-38112. S. Levine acknowledges partial support by NSF-DMS #0915219.
Gabriela Ghimpețeanu +3 more
openaire +3 more sources
Photogrammetric DSM denoising [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014 Abstract. Image matching techniques can nowadays provide very dense point clouds and they are often considered a valid alternative to LiDAR point cloud. However, photogrammetric point clouds are often characterized by a higher level of random noise compared to LiDAR data and by the presence of large outliers.
Markus Gerke, Francesco Nex
openaire +4 more sources
AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffusion
Julian Wyatt +3 more
semanticscholar +1 more source
Noise2Void - Learning Denoising From Single Noisy Images [PDF]
Computer Vision and Pattern Recognition, 2018 The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images. Recently it has been shown that such methods can also be trained without clean targets. Instead,
Alexander Krull +2 more
semanticscholar +1 more source
Applied Sciences, 2020 Although conventional denoising filters have been developed for noise reduction from digital images, these filters simultaneously cause blurring in the images. To address this problem, we proposed the fast non-local means (FNLM) denoising algorithm which
Bae-Guen Kim +4 more
doaj +1 more source
Stabilize, Decompose, and Denoise: Self-supervised Fluoroscopy Denoising
, 2022 11 pages, 18 ...
Ruizhou Liu +5 more
openaire +2 more sources