Results 31 to 40 of about 272,612 (359)
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
Youssef Mansour, Reinhard Heckel
semanticscholar +1 more source
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
Denoising Diffusion Bridge Models [PDF]
International Conference on Learning Representations, 2023 Diffusion models are powerful generative models that map noise to data using stochastic processes. However, for many applications such as image editing, the model input comes from a distribution that is not random noise.
Linqi Zhou +3 more
semanticscholar +1 more source
EURASIP Journal on Image and Video Processing, 2017 Background Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal).
Monagi H. Alkinani, Mahmoud R. El-Sakka
doaj +1 more source
Denoising Diffusion Samplers [PDF]
International Conference on Learning Representations, 2023 Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transform the data distribution into a Gaussian distribution.
Francisco Vargas +2 more
semanticscholar +1 more source
Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR
Remote Sensing, 2023 Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points.
Shuming Si +9 more
doaj +1 more source
PET image denoising based on denoising diffusion probabilistic model
European Journal of Nuclear Medicine and Molecular Imaging, 2023 8 ...
Kuang Gong +4 more
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Combined wavelet domain and motion compensated filtering compliant with video codecs [PDF]
, 2007 In this paper, we introduce the idea of using motion estimation resources from a video codec for video denoising. This is not straightforward because the motion estimators aimed for video compression and coding, tolerate errors in the estimated motion ...
Jovanov, Ljubomir +5 more
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AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network [PDF]
Computer Vision and Pattern Recognition, 2022 Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. Never-theless, they are still bound to synthetic noisy inputs due to less practical assumptions like pixel-wise independent noise.
Wooseok Lee, Sanghyun Son, Kyoung Mu Lee
semanticscholar +1 more source
Sensors, 2017 As the sound signal of ships obtained by sensors contains other many significant characteristics of ships and called ship-radiated noise (SN), research into a denoising algorithm and its application has obtained great significance. Using the advantage of
Yuxing Li, Yaan Li, Xiao Chen, Jing Yu
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