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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
IEEE Transactions on Image Processing, 2006We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries.
Michael Elad, M. Aharon
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Anomaly Detection with Conditioned Denoising Diffusion Models
German Conference on Pattern Recognition, 2023Traditional reconstruction-based methods have struggled to achieve competitive performance in anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a
Arian Mousakhan, T. Brox, Jawad Tayyub
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IEEE Transactions on Information Theory, 2000
Summary: The so-called denoising problem, relative to normal models for noise, is formalized such that ``noise'' is defined as the incompressible part in the data while the compressible part defines the meaningful information-bearing signal. Such a decomposition is effected by minimization of the ideal code length, called for by the minimum description
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Summary: The so-called denoising problem, relative to normal models for noise, is formalized such that ``noise'' is defined as the incompressible part in the data while the compressible part defines the meaningful information-bearing signal. Such a decomposition is effected by minimization of the ideal code length, called for by the minimum description
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Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven
IEEE Transactions on Neural Networks and Learning Systems, 2023Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications. In this technical review, we first give the noise analysis in different noisy HSIs and conclude crucial points for programming HSI denoising algorithms.
Qiang Zhang +5 more
semanticscholar +1 more source
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging domains, however, labeled data with pixel- or voxel-level label accuracy are scarce due to the cost of acquiring them. This problem is exacerbated further in domains like medical
Sean I. Young +6 more
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Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging domains, however, labeled data with pixel- or voxel-level label accuracy are scarce due to the cost of acquiring them. This problem is exacerbated further in domains like medical
Sean I. Young +6 more
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Hyperspectral Image Denoising via Tensor Low-Rank Prior and Unsupervised Deep Spatial–Spectral Prior
IEEE Transactions on Geoscience and Remote Sensing, 2022Hyperspectral image (HSI) denoising is a fundamental task in remote sensing image processing, which is helpful for HSI subsequent applications, such as unmixing and classification.
Wei Wu +4 more
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Improved Denoising Auto-Encoders for Image Denoising
2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018Image denoising is an important pre-processing step in image analysis. Various denoising algorithms, such as BM3D, PCD and K-SVD, obtain remarkable effects. Recently a deep denoising auto-encoder has been proposed and shown excellent performance compared to conventional image denoising algorithms.
Qian Xiang, Xuliang Pang
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Semi-Supervised DAS VSP Data Denoising Using Signal and Noise Distribution Difference
IEEE Transactions on Geoscience and Remote SensingDistributed acoustic sensing (DAS), an emerging technology for signal acquisition, has been progressively applied to collect vertical seismic profile (VSP) data.
Man Zhang +4 more
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Joint demosaicing and denoising
IEEE Transactions on Image Processing, 2005The output image of a digital camera is subject to a severe degradation due to noise in the image sensor. This paper proposes a novel technique to combine demosaicing and denoising procedures systematically into a single operation by exploiting their obvious similarities.
Keigo Hirakawa, T.W. Parks
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Comparison of the Wavelet Denoising Methods for Denoising of Phonocardiogram Signal [PDF]
, 2021 Heart diseases are the number one cause of death all over the world. Many deaths are caused due to late detection of heart diseases. During the process of heart sound recording, beside heart sound, environment noise is being recorded too. In this work signal denoising was performed using wavelet denoising method.
Jasmin Kevric +3 more
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