bm3d algorithm (Intel)
90
Structured Review
Intel
bm3d algorithm
Bm3d Algorithm, supplied by Intel, used in various techniques. Bioz Stars score: 90/100, based on 429 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/bm3d algorithm/product/Intel
Average 90 stars, based on 429 article reviews
Price from $9.99 to $1999.99
Bm3d Algorithm, supplied by Intel, used in various techniques. Bioz Stars score: 90/100, based on 429 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/bm3d algorithm/product/Intel
Average 90 stars, based on 429 article reviews
Price from $9.99 to $1999.99
bm3d algorithm - by Bioz Stars,
2020-07
90/100 stars
Images
1) Product Images from "Quasi noise-free digital holography"
Article Title: Quasi noise-free digital holography
Journal: Light, Science & Applications
doi: 10.1038/lsa.2016.142
Figure Legend Snippet: Dual wavelength DH. ( a , c , e , g ) color SLDH. ( b , d , f , h ) color MLDH-BM3D. Details of the image segments indicated by the white boxes are shown on the right side of the panel. The black box in h indicates the signal region where the noise measurements reported in column I of Table 1 were performed.
Techniques Used:
Figure Legend Snippet: Quantitative evaluation of the algorithm performance. ( a ) N C (%) vs. the number of looks, L , in the MLDH image (blue line), showing a ML improvement saturation. The theoretical ML improvement bound is plotted with a red line. The N C values corresponding to L =100 for MLDH, MLDH-NLM and MLDH-BM3D are indicated with a blue circle, an orange plus sign and a green cross, respectively. The plot shows the noise reduction percentage with respect to the noise level of the SLDH image (indicated by a red square). ( b ) N C (%) measured over the image segments A1-A5 of the SLDH (blue circles), MLDH (yellow triangles) and MLDH-BM3D (green crosses). The cascade of EG and SEF allows the theoretical ML improvement bound to be overcome and quasi noise-free DH reconstructions to be obtained.
Techniques Used:
Figure Legend Snippet: ( Supplementary Movie 3 ) Contrast analysis. ( a ) SLDH. ( b ) MLDH-BM3D. ( c ) Image contrast plotted along the lines indicated in the insets of a and b . Red: SLDH. Green: MLDH-BM3D. A close up of the flag stripes is also shown, demonstrating the capability of MLDH-BM3D to resolve all seven stripes on the US flag.
Techniques Used:
Figure Legend Snippet: Numerical MLDH-BM3D applied to color holograms (green and red) of a matryoshka doll. ( a , c ) SLDH. ( b , d ) MLDH-BM3D. ( e ) Relative deviation (R D ) visualization of selected details on the object highlighted by green and red boxes in a and c , respectively. ( f ) The same regions extracted from MLDH-BM3D reconstructions in b and d for a direct comparison. Yellow and blue boxes in b and d identify background regions and signal regions, respectively, over which the percentage image contrast is evaluated.
Techniques Used:
Figure Legend Snippet: Comparison between noisy and denoised DH reconstructions. ( a ) Noisy SLDH reconstruction. ( b ) MLDH. ( c ) SLDH-NLM cascade. ( d ) MLDH-NLM cascade. ( e ) SLDH-BM3D. ( f ) Quasi noise-free MLDH-BM3D reconstruction. ( g ) Details corresponding to SLDH (top row), MLDH-NLM (middle row) and MLDH-BM3D (bottom row). The enlarged areas correspond to the color boxes in a . In the bottom panels, the calculations of the percentage noise contrast improvements of MLDH-BM3D with respect to MLDH-NLM are reported.
Techniques Used:
Figure Legend Snippet: ( Supplementary Movies 1 and 2 ). Numerical MLDH-BM3D is applied to objects rotated by means of a stage, as sketched in ( a ) along with the corresponding photos. ( b , d ) SLDH. ( c , e ) MLDH-BM3D reconstructions. Details of the image segments indicated by yellow boxes are shown on the right side of the panel. As a result of denoising, MLDH-BM3D reconstructions show finer details, are better resolved and have improved sharpness on the edges and flatness over the homogeneous segments. Images in b , c are frames extracted from Supplementary Movie 2 . Images in d , e are extracted from Supplementary Movie 1 .
Techniques Used:
Figure Legend Snippet: Block scheme of the MLDH-BM3D algorithm. EG and SEF are sequential and complementary steps returning the MLDH-BM3D reconstruction.
Techniques Used: Blocking Assay
2) Product Images from "Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering"
Article Title: Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering
Journal: Biomedical Optics Express
doi: 10.1364/BOE.9.004569
Figure Legend Snippet: Reconstructed results of the tumor-mimicking tissue sample: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.
Techniques Used:
Figure Legend Snippet: Flowchart of the implementation of IRT-BM3D.
Techniques Used:
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.
Techniques Used:
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets in #60-view case with different SNR values: (a)-(d) UBP results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB; (e)-(h) IRT-BM3D results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB.
Techniques Used:
Figure Legend Snippet: Reconstructed images of the tumor-mimicking tissue sample: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k)-(l) Photographs of the biological tissue sample; (m) Reconstructed details in yellow dotted boxes in (e) and (j).
Techniques Used:
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k) Exact image of the phantom; (l)-(p) Profiles (along line A-A’) of the recovered images in #360-, #120-, #90-, #60- and #30-view cases.
Techniques Used:
Figure Legend Snippet: Reconstructed images of the ex vivo mouse intestinal tissue: (a) Photograph of the tissue sample; (b) #90-view IRT-BM3D result; (c) #90-view UBP result. The reconstructed details are shown in white dotted boxes.
Techniques Used: Ex Vivo
3) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"
Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU
Journal: BMC Bioinformatics
doi: 10.1186/s12859-016-0946-4
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on SSVFilter
Techniques Used:
4) Product Images from "Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region"
Article Title: Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region
Journal: PLoS ONE
doi: 10.1371/journal.pone.0133908
Figure Legend Snippet: 1. Shortest-Path Based Graph Search for Boundary Detection C n 1 C n 2 C n 8 . Note that boundaries are delineated with red, yellow, magenta, white, cyan, green black and blue solid lines, respectively and the notations are summarized in Table 1 . A parafoveal scan is chosen in order to show all the layers that are segmented by OCTRIMA 3D.
Techniques Used:
Figure Legend Snippet: OCTRIMA 3D framework to detect each intraretinal layer boundary using the shortest-path based graph search approach.
Techniques Used:
Figure Legend Snippet: The comparison between OCTRIMA 3D (red solid line) and the algorithm by Chiu et al. (blue solid line) using manual labeling as the ground truth (green solid line).
Techniques Used: Labeling
Figure Legend Snippet: Algorithms performance in the B-scan obtained from the patient with diabetic macular edema. (a) The raw OCT B-scan. (b) The boundaries delineated by the built-in Spectralis SD-OCT software for the ILM and RPE-CH. The yellow arrows are indicating the boundary detection errors by the built-in software of the Spectralis device. (c) The boundaries delineated by OCTRIMA 3D for the ILM and the RPE-CH.
Techniques Used: Software
Figure Legend Snippet: The overview of OCTRIMA 3D framework. The boundaries labeled using blue and red fonts have the dark-to-bright and bright-to-dark transitions, respectively.
Techniques Used: Labeling
Figure Legend Snippet: The comparison between Dufour’s Software (magenta solid line), IOWA reference algorithm (blue solid line) and OCTRIMA 3D (red solid line) using manual labeling as the ground truth (green solid line).
Techniques Used: Software, Labeling
Figure Legend Snippet: The segmentation results obtained for the B-scan in the eye with dry age-related macular degeneration using Dufour’s software and the OCTRIMA 3D algorithm. The legend of the boundaries is the same as Fig 1 . (a) THe raw OCT B-scan. (b) The segmentation result of Dufour’s software. The IS-OS delineation failed at the left most and center area of the B-scan. (c) The initial segmentation results of OCTRIMA 3D detected retinal boundaries reliably except for the IS-OS in the drusen area (green doted line). By adjusting the flattening step, the IS-OS is delineated correctly (green solid line).
Techniques Used: Software
Figure Legend Snippet: Comparison of unsigned segmentation errors on six surfaces between Dufour’s algorithm (left column), the IOWA reference algorithm (middle column) and OCTRIMA 3D (right column) in the ETDRS regions. The graph bar scale indicates the error magnitude in microns. The mean unsigned segmentation errors are reported in Table 2 .
Techniques Used:
5) Product Images from "Constrained distance transforms for spatial atlas registration"
Article Title: Constrained distance transforms for spatial atlas registration
Journal: BMC Bioinformatics
doi: 10.1186/s12859-015-0504-5
Figure Legend Snippet: 3D warp with tail flip. Clockwise from bottom left: Target volume, source volume, source warped to target using conventional RBF (truncated and rescaled), source warped to target using CDT, cut section through source warped using CDT and cut section through target. Landmarks are shown as spheres on the source and target volumes.
Techniques Used:
6) Product Images from "Local error estimates for adaptive simulation of the Reaction–Diffusion Master Equation via operator splitting"
Article Title: Local error estimates for adaptive simulation of the Reaction–Diffusion Master Equation via operator splitting
Journal: Journal of computational physics
doi: 10.1016/j.jcp.2014.02.004
Figure Legend Snippet: The figure shows the time steps selected by the adaptive DFSP method, along with the oscillation pattern of the membrane bound MinD protein for a representative trajectory. Note that the timestep adapts to the dynamics of the MinD oscillation. Due to
Techniques Used:
7) Product Images from "GPU-based Green’s function simulations of shear waves generated by an applied acoustic radiation force in elastic and viscoelastic models"
Article Title: GPU-based Green’s function simulations of shear waves generated by an applied acoustic radiation force in elastic and viscoelastic models
Journal: Physics in medicine and biology
doi: 10.1088/1361-6560/aabe36
Figure Legend Snippet: Computation times for serial and parallel implementations of time domain shear wave calculations with Green’s functions. The solid line with circle markers indicates the computation times for parallel simulations of shear waves with the elastic model, the dashed-dotted line with triangle markers indicates the computation times for parallel simulations of shear waves with the viscoelastic model, the dashed line with diamond markers indicates the computation times for serial simulations of shear waves with the elastic model, and the dotted line with square markers indicates the computation times for serial simulations of shear waves with the viscoelastic model.
Techniques Used:
8) Product Images from "MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions"
Article Title: MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions
Journal: BMC Bioinformatics
doi: 10.1186/s12859-015-0826-3
Figure Legend Snippet: Boxplots for the distributions of the TC values of MSAIndelFR and the other MSA algorithms using the BAliBASE 3.0 benchmark, where the top and bottom of a box and the line in between represent the third quartile, first quartile and median, respectively
Techniques Used:
Figure Legend Snippet: Boxplots for the distributions of the SP values of MSAIndelFR and the other MSA algorithms using the BAliBASE 3.0 benchmark, where the top and bottom of a box and the line in between represent the third quartile, first quartile and median, respectively
Techniques Used:
9) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"
Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU
Journal: BMC Bioinformatics
doi: 10.1186/s12859-016-0946-4
Figure Legend Snippet: Illustrations of proposed reordering and maximum functions for CUDAMPF. Assuming x 4 > x 3 > x 2 > x 1 after intra-warp reductions in ( b )
Techniques Used:
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on ViterbiFilter
Techniques Used:
Figure Legend Snippet: CUDAMPF program with NVRTC. After obtaining query model size and device properties, program dynamically makes decisions on unrolling innermost loop and selects the proper kernel file with compiler options
Techniques Used:
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on SSVFilter
Techniques Used:
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on MSVFilter
Techniques Used:
Figure Legend Snippet: CUDAMPF: Multi-tiered Parallel Framework on CUDA-enabled GPU. ( a ) A single GPU consists of n SMXs with m concurrently mounted blocks on each; ( b ) within each block, q resident warps are scheduled by x warp scheduler for processing assigned sequences; ( c ) a warp of threads score alignment of all residues and model states in parallel (warp size is fixed to 32 currently); ( d ) based on 32-bit register and score ranges of different algorithms, each thread processes multiple model states in a single step. The virtual boundary, block , is only regarded as the container of warps rather than a separate tier
Techniques Used: Blocking Assay
10) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"
Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU
Journal: BMC Bioinformatics
doi: 10.1186/s12859-016-0946-4
Figure Legend Snippet: CUDAMPF: Multi-tiered Parallel Framework on CUDA-enabled GPU. ( a ) A single GPU consists of n SMXs with m concurrently mounted blocks on each; ( b ) within each block, q resident warps are scheduled by x warp scheduler for processing assigned sequences; ( c ) a warp of threads score alignment of all residues and model states in parallel (warp size is fixed to 32 currently); ( d ) based on 32-bit register and score ranges of different algorithms, each thread processes multiple model states in a single step. The virtual boundary, block , is only regarded as the container of warps rather than a separate tier
Techniques Used: Blocking Assay
11) Product Images from "Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown"
Article Title: Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown
Journal: Nature protocols
doi: 10.1038/nprot.2016.095
Figure Legend Snippet: Transcript assembly and quantification with StringTie
Techniques Used:
12) Product Images from "Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown"
Article Title: Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie, and Ballgown
Journal: Nature protocols
doi: 10.1038/nprot.2016.095
Figure Legend Snippet: Box 2: Creating a HISAT2 index
Techniques Used:
13) Product Images from "Quasi noise-free digital holography"
Article Title: Quasi noise-free digital holography
Journal: Light, Science & Applications
doi: 10.1038/lsa.2016.142
Figure Legend Snippet: Dual wavelength DH. ( a , c , e , g ) color SLDH. ( b , d , f , h ) color MLDH-BM3D. Details of the image segments indicated by the white boxes are shown on the right side of the panel. The black box in h indicates the signal region where the noise measurements reported in column I of Table 1 were performed.
Techniques Used:
Figure Legend Snippet: Quantitative evaluation of the algorithm performance. ( a ) N C (%) vs. the number of looks, L , in the MLDH image (blue line), showing a ML improvement saturation. The theoretical ML improvement bound is plotted with a red line. The N C values corresponding to L =100 for MLDH, MLDH-NLM and MLDH-BM3D are indicated with a blue circle, an orange plus sign and a green cross, respectively. The plot shows the noise reduction percentage with respect to the noise level of the SLDH image (indicated by a red square). ( b ) N C (%) measured over the image segments A1-A5 of the SLDH (blue circles), MLDH (yellow triangles) and MLDH-BM3D (green crosses). The cascade of EG and SEF allows the theoretical ML improvement bound to be overcome and quasi noise-free DH reconstructions to be obtained.
Techniques Used:
Figure Legend Snippet: ( Supplementary Movie 3 ) Contrast analysis. ( a ) SLDH. ( b ) MLDH-BM3D. ( c ) Image contrast plotted along the lines indicated in the insets of a and b . Red: SLDH. Green: MLDH-BM3D. A close up of the flag stripes is also shown, demonstrating the capability of MLDH-BM3D to resolve all seven stripes on the US flag.
Techniques Used:
Figure Legend Snippet: Numerical MLDH-BM3D applied to color holograms (green and red) of a matryoshka doll. ( a , c ) SLDH. ( b , d ) MLDH-BM3D. ( e ) Relative deviation (R D ) visualization of selected details on the object highlighted by green and red boxes in a and c , respectively. ( f ) The same regions extracted from MLDH-BM3D reconstructions in b and d for a direct comparison. Yellow and blue boxes in b and d identify background regions and signal regions, respectively, over which the percentage image contrast is evaluated.
Techniques Used:
Figure Legend Snippet: Comparison between noisy and denoised DH reconstructions. ( a ) Noisy SLDH reconstruction. ( b ) MLDH. ( c ) SLDH-NLM cascade. ( d ) MLDH-NLM cascade. ( e ) SLDH-BM3D. ( f ) Quasi noise-free MLDH-BM3D reconstruction. ( g ) Details corresponding to SLDH (top row), MLDH-NLM (middle row) and MLDH-BM3D (bottom row). The enlarged areas correspond to the color boxes in a . In the bottom panels, the calculations of the percentage noise contrast improvements of MLDH-BM3D with respect to MLDH-NLM are reported.
Techniques Used:
Figure Legend Snippet: ( Supplementary Movies 1 and 2 ). Numerical MLDH-BM3D is applied to objects rotated by means of a stage, as sketched in ( a ) along with the corresponding photos. ( b , d ) SLDH. ( c , e ) MLDH-BM3D reconstructions. Details of the image segments indicated by yellow boxes are shown on the right side of the panel. As a result of denoising, MLDH-BM3D reconstructions show finer details, are better resolved and have improved sharpness on the edges and flatness over the homogeneous segments. Images in b , c are frames extracted from Supplementary Movie 2 . Images in d , e are extracted from Supplementary Movie 1 .
Techniques Used:
Figure Legend Snippet: Block scheme of the MLDH-BM3D algorithm. EG and SEF are sequential and complementary steps returning the MLDH-BM3D reconstruction.
Techniques Used: Blocking Assay
14) Product Images from "GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate"
Article Title: GLIMMPSE: Online Power Computation for Linear Models with and without a Baseline Covariate
Journal: Journal of statistical software
doi:
Figure Legend Snippet: GLIMMPSE results screen.
Techniques Used:
Figure Legend Snippet: GLIMMPSE mode selection screen.
Techniques Used: Selection
Figure Legend Snippet: 3.2. How to use GLIMMPSE
Techniques Used:
Figure Legend Snippet: Overview of the GLIMMPSE architecture.
Techniques Used:
15) Product Images from "GPU Acceleration of Optical Mapping Algorithm for Cardiac Electrophysiology"
Article Title: GPU Acceleration of Optical Mapping Algorithm for Cardiac Electrophysiology
Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
doi: 10.1109/EMBC.2012.6346240
Figure Legend Snippet: The performance of the GPU implementation in comparison to the Matlab CPU implementation, the serial C++ implementation and the OpenMP C++ implementation.
Techniques Used:
16) Product Images from "Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering"
Article Title: Enhancing sparse-view photoacoustic tomography with combined virtually parallel projecting and spatially adaptive filtering
Journal: Biomedical Optics Express
doi: 10.1364/BOE.9.004569
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(c) UBP, TV-GD and IRT-BM3D results for #60-view case; (d)-(f) UBP, TV-GD and IRT-BM3D results for #30-view case.
Techniques Used:
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets in #60-view case with different SNR values: (a)-(d) UBP results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB; (e)-(h) IRT-BM3D results in the cases of SNR = 5 dB, 10 dB, 15 dB and 20 dB.
Techniques Used:
Figure Legend Snippet: Reconstructed results of the vessel phantom with tumor targets: (a)-(e) UBP results of #360-, #120-, #90- #60- and #30-view cases; (f)-(j) IRT-BM3D results of #360-, #120-, #90-, #60- and #30-view cases; (k) Exact image of the phantom; (l)-(p) Profiles (along line A-A’) of the recovered images in #360-, #120-, #90-, #60- and #30-view cases.
Techniques Used:
Figure Legend Snippet: Reconstructed images of the ex vivo mouse intestinal tissue: (a) Photograph of the tissue sample; (b) #90-view IRT-BM3D result; (c) #90-view UBP result. The reconstructed details are shown in white dotted boxes.
Techniques Used: Ex Vivo
17) Product Images from "CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU"
Article Title: CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU
Journal: BMC Bioinformatics
doi: 10.1186/s12859-016-0946-4
Figure Legend Snippet: Evaluations of CUDAMPF with Intel Xeon, i5 and i7 on ViterbiFilter
Techniques Used:
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