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bicubic kernel function  (MathWorks Inc)


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    MathWorks Inc bicubic kernel function
    Comparison of <t> bicubic, </t> overcomplete dictionaries, MRBT‐SR‐without perceptual loss, MRBT‐SR‐with perceptual loss on benchmark data
    Bicubic Kernel Function, supplied by MathWorks Inc, 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/bicubic kernel function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    bicubic kernel function - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Super‐resolution of brain tumor MRI images based on deep learning"

    Article Title: Super‐resolution of brain tumor MRI images based on deep learning

    Journal: Journal of Applied Clinical Medical Physics

    doi: 10.1002/acm2.13758

    Comparison of bicubic, overcomplete dictionaries, MRBT‐SR‐without perceptual loss, MRBT‐SR‐with perceptual loss on benchmark data
    Figure Legend Snippet: Comparison of bicubic, overcomplete dictionaries, MRBT‐SR‐without perceptual loss, MRBT‐SR‐with perceptual loss on benchmark data

    Techniques Used: Comparison

    Results of super‐resolution methods: (a) 4× downsampling of the original MRI image, (b) bicubic upsampling, (c) overcomplete dictionaries, (d) enhanced super‐resolution generative adversarial networks, (e) MRI‐based brain tumor super‐resolution (MRBT‐SR) with visual geometry group perceptual loss, (f) MRBT‐SR without perceptual loss, (g) MRBT‐SR with perceptual loss (Stage 1), (h) MRBT‐SR with perceptual loss (Stage 2), (i) MRBT‐SR with perceptual loss (Stage 3), (j) MRBT‐SR with perceptual loss (Stage 4), (k) the original high‐resolution image
    Figure Legend Snippet: Results of super‐resolution methods: (a) 4× downsampling of the original MRI image, (b) bicubic upsampling, (c) overcomplete dictionaries, (d) enhanced super‐resolution generative adversarial networks, (e) MRI‐based brain tumor super‐resolution (MRBT‐SR) with visual geometry group perceptual loss, (f) MRBT‐SR without perceptual loss, (g) MRBT‐SR with perceptual loss (Stage 1), (h) MRBT‐SR with perceptual loss (Stage 2), (i) MRBT‐SR with perceptual loss (Stage 3), (j) MRBT‐SR with perceptual loss (Stage 4), (k) the original high‐resolution image

    Techniques Used:

    Comparison of improved performance contributed to brain tumor segmentation using different super‐resolution methods
    Figure Legend Snippet: Comparison of improved performance contributed to brain tumor segmentation using different super‐resolution methods

    Techniques Used: Comparison



    Similar Products

    bicubic kernel function  (MathWorks Inc)
    90
    MathWorks Inc bicubic kernel function
    Comparison of <t> bicubic, </t> overcomplete dictionaries, MRBT‐SR‐without perceptual loss, MRBT‐SR‐with perceptual loss on benchmark data
    Bicubic Kernel Function, supplied by MathWorks Inc, 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/bicubic kernel function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    bicubic kernel function - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Comparison of bicubic, overcomplete dictionaries, MRBT‐SR‐without perceptual loss, MRBT‐SR‐with perceptual loss on benchmark data

    Journal: Journal of Applied Clinical Medical Physics

    Article Title: Super‐resolution of brain tumor MRI images based on deep learning

    doi: 10.1002/acm2.13758

    Figure Lengend Snippet: Comparison of bicubic, overcomplete dictionaries, MRBT‐SR‐without perceptual loss, MRBT‐SR‐with perceptual loss on benchmark data

    Article Snippet: Each input axial slice of an MRI T2 FLAIR image was normalized through the following steps: (1) the mean intensity value and the standard deviation of the foreground pixels were calculated, (2) the intensity value was subtracted by mean intensity value, and then divided by the standard deviation value for each pixel (including the background pixels), and (3) the high‐resolution normalized images were downsampled by a scaling factor of four using the MATLAB bicubic kernel function.

    Techniques: Comparison

    Results of super‐resolution methods: (a) 4× downsampling of the original MRI image, (b) bicubic upsampling, (c) overcomplete dictionaries, (d) enhanced super‐resolution generative adversarial networks, (e) MRI‐based brain tumor super‐resolution (MRBT‐SR) with visual geometry group perceptual loss, (f) MRBT‐SR without perceptual loss, (g) MRBT‐SR with perceptual loss (Stage 1), (h) MRBT‐SR with perceptual loss (Stage 2), (i) MRBT‐SR with perceptual loss (Stage 3), (j) MRBT‐SR with perceptual loss (Stage 4), (k) the original high‐resolution image

    Journal: Journal of Applied Clinical Medical Physics

    Article Title: Super‐resolution of brain tumor MRI images based on deep learning

    doi: 10.1002/acm2.13758

    Figure Lengend Snippet: Results of super‐resolution methods: (a) 4× downsampling of the original MRI image, (b) bicubic upsampling, (c) overcomplete dictionaries, (d) enhanced super‐resolution generative adversarial networks, (e) MRI‐based brain tumor super‐resolution (MRBT‐SR) with visual geometry group perceptual loss, (f) MRBT‐SR without perceptual loss, (g) MRBT‐SR with perceptual loss (Stage 1), (h) MRBT‐SR with perceptual loss (Stage 2), (i) MRBT‐SR with perceptual loss (Stage 3), (j) MRBT‐SR with perceptual loss (Stage 4), (k) the original high‐resolution image

    Article Snippet: Each input axial slice of an MRI T2 FLAIR image was normalized through the following steps: (1) the mean intensity value and the standard deviation of the foreground pixels were calculated, (2) the intensity value was subtracted by mean intensity value, and then divided by the standard deviation value for each pixel (including the background pixels), and (3) the high‐resolution normalized images were downsampled by a scaling factor of four using the MATLAB bicubic kernel function.

    Techniques:

    Comparison of improved performance contributed to brain tumor segmentation using different super‐resolution methods

    Journal: Journal of Applied Clinical Medical Physics

    Article Title: Super‐resolution of brain tumor MRI images based on deep learning

    doi: 10.1002/acm2.13758

    Figure Lengend Snippet: Comparison of improved performance contributed to brain tumor segmentation using different super‐resolution methods

    Article Snippet: Each input axial slice of an MRI T2 FLAIR image was normalized through the following steps: (1) the mean intensity value and the standard deviation of the foreground pixels were calculated, (2) the intensity value was subtracted by mean intensity value, and then divided by the standard deviation value for each pixel (including the background pixels), and (3) the high‐resolution normalized images were downsampled by a scaling factor of four using the MATLAB bicubic kernel function.

    Techniques: Comparison