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deep neural networks (dnn) approach  (Sanofi)

 
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    Sanofi deep neural networks (dnn) approach
    Deep Neural Networks (Dnn) Approach, supplied by Sanofi, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/deep neural networks (dnn) approach/product/Sanofi
    Average 90 stars, based on 1 article reviews
    deep neural networks (dnn) approach - by Bioz Stars, 2026-06
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    deep neural networks (dnn) approach  (Sanofi)
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    Sanofi deep neural networks (dnn) approach
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    deep neural network (dnn) denoising approach  (MathWorks Inc)
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    MathWorks Inc deep neural network (dnn) denoising approach
    A comparison of <t>spatial</t> <t>denoising</t> techniques as measured by pSNR Gain (normalized to pSNR of the input data) and SSIM for two different pixel sizes representing HD and standard definition imaging magnifications and two spectral bands of 3300 cm −1 and 1650 cm −1 , corresponding to 3 and 6 µm wavelengths. Seven methods were tested: Fourier transform (FT), Mean Filter (Mean), Gauss Filter (Gauss), Median Filter (Median), Weighted Mean (W. Mean), spatial Wavelets (Wavelets) and Deep Neural Networks <t>(DNN).</t> The size of the dots corresponds to the initial noise level, starting with the highest noise for the largest dot (2 scans) and going down to the smallest (256 scans). The optimal parameters for each of the methods and noise levels are given in Supplementary Materials.
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    A comparison of spatial denoising techniques as measured by pSNR Gain (normalized to pSNR of the input data) and SSIM for two different pixel sizes representing HD and standard definition imaging magnifications and two spectral bands of 3300 cm −1 and 1650 cm −1 , corresponding to 3 and 6 µm wavelengths. Seven methods were tested: Fourier transform (FT), Mean Filter (Mean), Gauss Filter (Gauss), Median Filter (Median), Weighted Mean (W. Mean), spatial Wavelets (Wavelets) and Deep Neural Networks (DNN). The size of the dots corresponds to the initial noise level, starting with the highest noise for the largest dot (2 scans) and going down to the smallest (256 scans). The optimal parameters for each of the methods and noise levels are given in Supplementary Materials.

    Journal: Scientific Reports

    Article Title: Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

    doi: 10.1038/s41598-018-32713-7

    Figure Lengend Snippet: A comparison of spatial denoising techniques as measured by pSNR Gain (normalized to pSNR of the input data) and SSIM for two different pixel sizes representing HD and standard definition imaging magnifications and two spectral bands of 3300 cm −1 and 1650 cm −1 , corresponding to 3 and 6 µm wavelengths. Seven methods were tested: Fourier transform (FT), Mean Filter (Mean), Gauss Filter (Gauss), Median Filter (Median), Weighted Mean (W. Mean), spatial Wavelets (Wavelets) and Deep Neural Networks (DNN). The size of the dots corresponds to the initial noise level, starting with the highest noise for the largest dot (2 scans) and going down to the smallest (256 scans). The optimal parameters for each of the methods and noise levels are given in Supplementary Materials.

    Article Snippet: Finally, Artificial Neural Networks can be used for denoising and recently the first Deep Neural Network (DNN) denoising approach was enabled in Matlab packages and is applied here to IR data for the first time.

    Techniques: Comparison, Imaging

    Details of spatial denoising results for HD data with Fourier transform (FT) and Deep Neural Networks (DNN) shown in details as they performed relatively the best (all images are given in Supplementary Materials S10). Zoom-ins on tissue structure and edges are shown to better highlight noise rejection and potential artifacts introduced by a given method.

    Journal: Scientific Reports

    Article Title: Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

    doi: 10.1038/s41598-018-32713-7

    Figure Lengend Snippet: Details of spatial denoising results for HD data with Fourier transform (FT) and Deep Neural Networks (DNN) shown in details as they performed relatively the best (all images are given in Supplementary Materials S10). Zoom-ins on tissue structure and edges are shown to better highlight noise rejection and potential artifacts introduced by a given method.

    Article Snippet: Finally, Artificial Neural Networks can be used for denoising and recently the first Deep Neural Network (DNN) denoising approach was enabled in Matlab packages and is applied here to IR data for the first time.

    Techniques:

    Results of spatial and spectral denoising on the image quality of a healthy pancreatic tissue sample. Median, FT, DNN, PCA and MNF denoising methods were applied to an experimental image acquired with 4 scans.

    Journal: Scientific Reports

    Article Title: Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data

    doi: 10.1038/s41598-018-32713-7

    Figure Lengend Snippet: Results of spatial and spectral denoising on the image quality of a healthy pancreatic tissue sample. Median, FT, DNN, PCA and MNF denoising methods were applied to an experimental image acquired with 4 scans.

    Article Snippet: Finally, Artificial Neural Networks can be used for denoising and recently the first Deep Neural Network (DNN) denoising approach was enabled in Matlab packages and is applied here to IR data for the first time.

    Techniques: