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Feature-Preserving Noise Removal
IEEE Transactions on Medical Imaging, 2015Conventional image restoration algorithms use transform-domain filters, which separate the noise from the sparse signal among the transform components or apply spatial smoothing filters in real space whose design relies on prior assumptions about the noise statistics. These filters also reduce the information content of the image by suppressing spatial
Khalid, Youssef +2 more
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Concurrency and Computation, 2021
The speckle noise present in synthetic‐aperture radar (SAR) images is responsible for hindering the extraction of the exact information that needs to be utilized for potential remote sensing applications.
Mohan Ellappan +5 more
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The speckle noise present in synthetic‐aperture radar (SAR) images is responsible for hindering the extraction of the exact information that needs to be utilized for potential remote sensing applications.
Mohan Ellappan +5 more
semanticscholar +1 more source
A nonlocal low rank model for poisson noise removal
Inverse Problems and Imaging, 2021Patch-based methods, which take the advantage of the redundancy and similarity among image patches, have attracted much attention in recent years. However, these methods are mainly limited to Gaussian noise removal.
Mingchao Zhao +3 more
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A Speckle Noise Removal Method
Circuits, Systems, and Signal Processing, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Saadia, Ayesha, Rashdi, Adnan
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IEEE Transactions on Geoscience and Remote Sensing, 2020
As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many subsequent applications. Recently, based on the framework of subspace representation and low-rank matrix/tensor factorization (LRMF/LRTF), many single-factor ...
Yu-Bang Zheng +4 more
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As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many subsequent applications. Recently, based on the framework of subspace representation and low-rank matrix/tensor factorization (LRMF/LRTF), many single-factor ...
Yu-Bang Zheng +4 more
semanticscholar +1 more source
2001
Proceedings of Graphics Interface 2001, Ottawa, Ontario, Canada, Canada, 7 - 9 June 2001, 19 ...
Guskov, Igor, Wood, Zoe
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Proceedings of Graphics Interface 2001, Ottawa, Ontario, Canada, Canada, 7 - 9 June 2001, 19 ...
Guskov, Igor, Wood, Zoe
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CCD noise removal in digital images
IEEE Transactions on Image Processing, 2006In this work, we propose a denoising scheme to restore images degraded by CCD noise. The CCD noise model, measured in the space of incident light values (light space), is a combination of signal-independent and signal-dependent noise terms. This model becomes more complex in image brightness space (normal camera output) due to the nonlinearity of the ...
Hilda, Faraji, W James, MacLean
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ℓ1 − αℓ2 minimization methods for signal and image reconstruction with impulsive noise removal
Inverse Problems, 2020In this paper, we study ℓ1 − αℓ2 (0 < α ⩽ 1) minimization methods for signal and image reconstruction with impulsive noise removal. The data fitting term is based on ℓ1 fidelity between the reconstruction output and the observational data, and the ...
Peng Li, Wengu Chen, Huanmin Ge, M. Ng
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IEEE Transactions on Image Processing, 2001
A generic n-dimensional filter with the primary purpose of eliminating impulsive-like noise is presented. This recursive nonlinear filter is composed of two conditional rules, which are applied independently, in any order, one after the other. It identifies noisy items by inspection of their surrounding neighborhood, and afterwards it replaces their ...
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A generic n-dimensional filter with the primary purpose of eliminating impulsive-like noise is presented. This recursive nonlinear filter is composed of two conditional rules, which are applied independently, in any order, one after the other. It identifies noisy items by inspection of their surrounding neighborhood, and afterwards it replaces their ...
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EEG Ocular Artefacts and Noise Removal
2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007The general framework of this research is the pre-processing of the electroencephalographic (EEG) signals. The goal of this paper is to compare several combinations of wavelet denoising (WD) and independent component analysis (ICA) algorithms for noise and artefacts removal. These methods are tested on simulated EEG, using different evaluation criteria.
R, Romo-Vazquez +3 more
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