Results 21 to 30 of about 272,612 (359)
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
doaj +1 more source
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection [PDF]
Computer Vision and Pattern Recognition, 2022 Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S- T) framework has proved to be effective in solving this chal-lenge.
Xuan Zhang +5 more
semanticscholar +1 more source
Hyperspectral Image Denoising Based on Multi-Stream Denoising Network [PDF]
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021 Hyperspectral images (HSIs) have been widely applied in many fields, such as military, agriculture, and environment monitoring. Nevertheless, HSIs commonly suffer from various types of noise during acquisition. Therefore, denoising is critical for HSI analysis and applications.
Gao, Yan, Gao, Feng, Dong, Junyu
openaire +2 more sources
Hybridization between deep learning algorithms and neutrosophic theory in medical image processing: A survey [PDF]
Neutrosophic Sets and Systems, 2021 Deep learning can successfully extract data features based on dealing greatly with nonlinear problems. Deep learning has the highest performance in medical image analysis and diagnosis.
N.N. Mostafa, K. Ahmed, I. El-Henawy
doaj +1 more source
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models [PDF]
IEEE International Conference on Computer Vision, 2021 Denoising diffusion probabilistic models (DDPM) have shown remarkable performance in unconditional image generation. However, due to the stochasticity of the generative process in DDPM, it is challenging to generate images with the desired semantics.
Jooyoung Choi +4 more
semanticscholar +1 more source
Hourly Flood Forecasting Using Hybrid Wavelet-SVM [PDF]
Journal of Soft Computing in Civil Engineering, 2022 The floods of 2018 and 2019 have underlined the urgent need for development and implementation of efficient and robust flood forecasting models for the major rivers in the State of Kerala, India.
Baheerah Shada +2 more
doaj +1 more source
FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising [PDF]
IEEE Transactions on Image Processing, 2017 Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with ...
K. Zhang, W. Zuo, Lei Zhang
semanticscholar +1 more source
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
doaj +1 more source
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
doaj +1 more source
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images [PDF]
Computer Vision and Pattern Recognition, 2021 In the last few years, image denoising has benefited a lot from the fast development of neural networks. However, the requirement of large amounts of noisy-clean image pairs for supervision limits the wide use of these models.
Tao Huang +4 more
semanticscholar +1 more source