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bicubic kernel function  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
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2026-03
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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
Techniques: Comparison
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
Techniques:
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
Techniques: Comparison