Results 221 to 230 of about 49,854 (268)
A Denoising Preprocessing Framework via Orthogonal Multi-Tap Null-Steering Beamformer Bank: Facilitating Target Signal Preservation Under Low SINR Conditions and Complex Soundscapes. [PDF]
Sensors (Basel)Chen L, Xu Z, Su P, Zhao Z.
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A Frequency-Aware Self-Supervised Framework for MEMS-OCT Denoising. [PDF]
Biosensors (Basel)Zhang G, Li Z, Zhao H, Peng Z, Xie H.
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DiSCO: deconvoluting spatial transcriptomics via combinatorial optimization with a foundational diffusion model. [PDF]
Brief BioinformLiu J, Wu Y, Li L.
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Expanding the Capabilities of Portable Mapping in Macroscopic External Reflection FT-IR through a Targeted Data-Driven Spectral Enhancement and Denoising Strategy. [PDF]
ACS Meas Sci AuLi Z +7 more
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Computational Statistics, 2007
Iterative denoising is a data mining technology for analysis of large heterogeneous datasets, e.g., sets of text documents. The result of it is a hierarchical divisive cluster tree with visual representation of each node. The specific feature of this technology is that the features for the clustering are extracted from data at each node of the ...
Kendall E. Giles +3 more
openaire +1 more source
Iterative denoising is a data mining technology for analysis of large heterogeneous datasets, e.g., sets of text documents. The result of it is a hierarchical divisive cluster tree with visual representation of each node. The specific feature of this technology is that the features for the clustering are extracted from data at each node of the ...
Kendall E. Giles +3 more
openaire +1 more source
IEEE Transactions on Image Processing, 2010
Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable.
Priyam Chatterjee, Peyman Milanfar
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Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. Recently proposed methods take different approaches to the problem and yet their denoising performances are comparable.
Priyam Chatterjee, Peyman Milanfar
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2017
The filling-in effect of diffusion processes has been successfully used in many image analysis applications. Examples include image reconstructions in inpainting-based compression or dense optic flow computations. As an interesting side effect of diffusion-based inpainting, the interpolated data are smooth, even if the known image data are noisy ...
Robin Dirk Adam +2 more
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The filling-in effect of diffusion processes has been successfully used in many image analysis applications. Examples include image reconstructions in inpainting-based compression or dense optic flow computations. As an interesting side effect of diffusion-based inpainting, the interpolated data are smooth, even if the known image data are noisy ...
Robin Dirk Adam +2 more
openaire +1 more source
2007
We consider the problem of denoising a noisily sampled submanifold M in R^d, where the submanifold M is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample.
Hein, M., Maier, M.
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We consider the problem of denoising a noisily sampled submanifold M in R^d, where the submanifold M is a priori unknown and we are only given a noisy point sample. The presented denoising algorithm is based on a graph-based diffusion process of the point sample.
Hein, M., Maier, M.
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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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IEEE Transactions on Image Processing, 2014
Image denoising continues to be an active research topic. Although state-of-the-art denoising methods are numerically impressive and approch theoretical limits, they suffer from visible artifacts.While they produce acceptable results for natural images, human eyes are less forgiving when viewing synthetic images.
Claude Knaus, Matthias Zwicker
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Image denoising continues to be an active research topic. Although state-of-the-art denoising methods are numerically impressive and approch theoretical limits, they suffer from visible artifacts.While they produce acceptable results for natural images, human eyes are less forgiving when viewing synthetic images.
Claude Knaus, Matthias Zwicker
openaire +2 more sources