├── .gitignore ├── scripts ├── .gitignore ├── build_forward_operator.m ├── read_2020_Nov_18.m └── read_2020_Feb_04.m ├── setup.jpg ├── functions └── print_info_md.m └── readMe.md /.gitignore: -------------------------------------------------------------------------------- 1 | data/ -------------------------------------------------------------------------------- /scripts/.gitignore: -------------------------------------------------------------------------------- 1 | *.asv -------------------------------------------------------------------------------- /setup.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/openspyrit/spihim/HEAD/setup.jpg -------------------------------------------------------------------------------- /scripts/build_forward_operator.m: -------------------------------------------------------------------------------- 1 | % Build the forward operator corresponding to the SPIHIM datasets. 2 | % It requires some SPIRIT functions. 3 | 4 | % Author: N Ducros 5 | % Institution: University of Lyon, CREATIS 6 | % Last Update: 20-May-2021 7 | % 8 | % This code is given freely under Creative Commons %Attribution-ShareAlike 9 | % 4.0 International license (CC-BY-SA 4.0) 10 | % http://creativecommons.org/licenses/by-sa/4.0/ 11 | 12 | N = 64; 13 | 14 | %% Full Hadamard matrix 15 | H = hadpatmat(N); 16 | 17 | %% Save 18 | filename = fullfile('../data',sprintf('Hadamard_64x64_forward_stl10_unlabeled.mat')); 19 | save(filename,'H'); 20 | 21 | %% Read data 22 | myfolder = 'D:\Creatis\Programmes\Experimental\SPAS\Data\2020_Jan_C'; 23 | savingName = 'siemensStar2b'; 24 | [M,opt,par,spec] = read_Hadamard_spectro(myfolder, savingName); 25 | 26 | %% H: full forward, m0: measurement vector padded with zeros 27 | m0 = reshape(sum(M,3),[],1); 28 | f0 = H'*m0; 29 | figure; imagesc(reshape(f0,64,64)); axis image 30 | 31 | %% H1: reduced forward, m1: acquired coefficients only 32 | ind_opt = par.pattern_indices(2:2:end)/2; 33 | m1 = m0(ind_opt); 34 | H1 = H(ind_opt,:); 35 | f1 = H1'*m1; 36 | figure; imagesc(reshape(f1,64,64)); axis image -------------------------------------------------------------------------------- /functions/print_info_md.m: -------------------------------------------------------------------------------- 1 | % PRINT_INFO_MD Print dataset info to generat table for markdown file 2 | % 3 | % READ_HADAMARD_SPECTRO(FOLDER, SAVING_NAME) 4 | % reads the dataset SAVING_NAME the folder FOLDER 5 | % 6 | % READ_HADAMARD_SPECTRO(..., plotopt) specifies the plot option 7 | % - plotopt = 'grayscale' to plot a grayscale image 8 | % - plotopt = 'color' to plot a color image 9 | % 10 | % READ_HADAMARD_SPECTRO(..., lambda_min, lambda_max) specifies the 11 | % spectral range for color plot, by default [430-680] nm. 12 | % 13 | % -------- 14 | % Example. 15 | % PRINT_INFO_MD; 16 | 17 | % Author: N Ducros 18 | % Institution: University of Lyon, CREATIS 19 | % Last Update: 30-Nov-2020 20 | % 21 | % This code is given freely under Creative Commons %Attribution-ShareAlike 22 | % 4.0 International license (CC-BY-SA 4.0) 23 | % http://creativecommons.org/licenses/by-sa/4.0/ 24 | 25 | 26 | 27 | function print_info_md(folder) 28 | 29 | %% Get filenames 30 | if nargin == 1, dirmat = dir(fullfile(folder,'*_raw.mat')); end 31 | if nargin == 0, folder='./'; dirmat = dir(fullfile(folder,'*_raw.mat')); end 32 | 33 | disp('Filename | $`M`$ | $`\Delta t`$ (ms) | Comment |'); 34 | disp('|--|--:|--:|--|'); 35 | 36 | %% 37 | for ii=1:length(dirmat) 38 | 39 | %% LOAD mat-file and print main acquisition parametres 40 | load(fullfile(folder, dirmat(ii).name)); 41 | fprintf('%s | %i | %i | |\n', dirmat(ii).name, par.number_patterns, par.CT*1e3); 42 | 43 | end -------------------------------------------------------------------------------- /scripts/read_2020_Nov_18.m: -------------------------------------------------------------------------------- 1 | % Read the measurements acquired during the 18-Nov-2020 session. 2 | % First measurement with Laurent using fast acquisition Labview software 3 | 4 | % Author: N Ducros 5 | % Institution: University of Lyon, CREATIS 6 | % Last Update: 25-Nov-20 7 | % 8 | % This code is given freely under Creative Commons %Attribution-ShareAlike 9 | % 4.0 International license (CC-BY-SA 4.0) 10 | % http://creativecommons.org/licenses/by-sa/4.0/ 11 | 12 | 13 | %% User-defined 14 | clc; close all; clear; 15 | folder = 'D:\Creatis\Programmes\Experimental\SPAS\Data\2020_Nov'; %% Please Update!! 16 | 17 | %% Read no object, lamp no diffuser 18 | savingName = 'noObject_10ms'; 19 | 20 | figure('Name', savingName); 21 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 22 | 23 | %% Read no object, lamp no diffuser 24 | savingName = 'noObject_01ms'; 25 | 26 | figure('Name', savingName); 27 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 28 | 29 | %% Read linear variable filter 30 | savingName = 'variableFilter_10ms'; 31 | 32 | figure('Name', savingName); 33 | readRecPlot_Hadamard_spectro(folder, savingName,'Color',600,700); 34 | 35 | %% Read linear variable filter 36 | savingName = 'variableFilter_01ms'; 37 | 38 | figure('Name', savingName); 39 | readRecPlot_Hadamard_spectro(folder, savingName,'Color',600,700); 40 | 41 | %% Read linear variable filter 42 | savingName = 'variableFilter_B_10ms'; 43 | 44 | figure('Name', savingName); 45 | readRecPlot_Hadamard_spectro(folder,savingName, 'Color',450,550); 46 | 47 | %% Read linear variable filter 48 | savingName = 'variableFilter_B_01ms'; 49 | 50 | figure('Name', savingName); 51 | readRecPlot_Hadamard_spectro(folder, savingName,'Color',450,550); 52 | 53 | %% Read linear variable filter 54 | nflip = 10; % integration time in ms 55 | savingName = 'variableFilter_C_10ms'; 56 | 57 | figure('Name', savingName); 58 | readRecPlot_Hadamard_spectro(folder, savingName,'Color',520,620); 59 | 60 | %% Star sector 61 | savingName = 'starSector_01ms'; 62 | 63 | figure('Name', savingName); 64 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 65 | 66 | %% Star sector with variable filter 67 | savingName = 'starSector_variableFilter_C_10ms'; 68 | 69 | figure('Name', savingName); 70 | readRecPlot_Hadamard_spectro(folder, savingName,'Color',520,620); 71 | -------------------------------------------------------------------------------- /scripts/read_2020_Feb_04.m: -------------------------------------------------------------------------------- 1 | % Read the measurements acquired during the 18-Nov-2020 session. 2 | 3 | % Author: N Ducros 4 | % Institution: University of Lyon, CREATIS 5 | % Last Update: 25-Nov-20 6 | % 7 | % This code is given freely under Creative Commons %Attribution-ShareAlike 8 | % 4.0 International license (CC-BY-SA 4.0) 9 | % http://creativecommons.org/licenses/by-sa/4.0/ 10 | 11 | 12 | %% User-defined 13 | clc; close all; clear; 14 | folder = 'D:\Creatis\Programmes\Experimental\SPAS\Data\2020_Jan_C'; %% Please Update!! 15 | 16 | %% Read no object 17 | savingName = 'whiteLED1a'; 18 | 19 | figure('Name', savingName); 20 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 21 | 22 | %% Read paper sheet 23 | savingName = 'paperSheet1b'; 24 | 25 | figure('Name', savingName); 26 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 27 | 28 | %% Read paper sheet 29 | savingName = 'paperSheet2b'; 30 | 31 | figure('Name', savingName); 32 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 33 | 34 | %% Read paper sheet 35 | savingName = 'paperSheet4b'; 36 | 37 | figure('Name', savingName); 38 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 39 | 40 | %% Read grayscale siemens star target 41 | savingName = 'siemensStar1a'; 42 | 43 | figure('Name', savingName); 44 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 45 | 46 | %% Read grayscale siemens star target 47 | savingName = 'siemensStar1b'; 48 | 49 | figure('Name', savingName); 50 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 51 | 52 | %% Read grayscale siemens star target 53 | savingName = 'siemensStar2a'; 54 | 55 | figure('Name', savingName); 56 | readRecPlot_Hadamard_spectro(folder,savingName, 'Color'); 57 | 58 | %% Read grayscale siemens star target 59 | savingName = 'siemensStar2b'; 60 | 61 | figure('Name', savingName); 62 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 63 | 64 | %% Read grayscale siemens star target 65 | savingName = 'siemensStar4a'; 66 | 67 | figure('Name', savingName); 68 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 69 | 70 | %% Read grayscale siemens star target 71 | savingName = 'siemensStar4b'; 72 | 73 | figure('Name', savingName); 74 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 75 | 76 | %% Read grayscale siemens star target 77 | savingName = 'siemensStar8a'; 78 | 79 | figure('Name', savingName); 80 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 81 | 82 | %% Read grayscale siemens star target 83 | savingName = 'siemensStar8b'; 84 | 85 | figure('Name', savingName); 86 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 87 | 88 | 89 | %% Read COLOR siemens star target 90 | savingName = 'siemensStarColor1b'; 91 | 92 | figure('Name', savingName); 93 | readRecPlot_Hadamard_spectro(folder,savingName, 'Color'); 94 | 95 | %% Read COLOR siemens star target 96 | savingName = 'siemensStarColor2b'; 97 | 98 | figure('Name', savingName); 99 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 100 | 101 | %% Read COLOR siemens star target 102 | savingName = 'siemensStarColor4b'; 103 | 104 | figure('Name', savingName); 105 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 106 | 107 | %% Read COLOR siemens star target 108 | savingName = 'siemensStarColor4c'; 109 | 110 | figure('Name', savingName); 111 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 112 | 113 | %% Read COLOR siemens star target 114 | savingName = 'siemensStarColor8d'; 115 | 116 | figure('Name', savingName); 117 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 118 | 119 | %% Read COLOR siemens star target 120 | savingName = 'SiemensColor8a'; 121 | 122 | figure('Name', savingName); 123 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 124 | 125 | %% Read COLOR siemens star target 126 | savingName = 'SiemensColor8b'; 127 | 128 | figure('Name', savingName); 129 | readRecPlot_Hadamard_spectro(folder, savingName,'Color'); 130 | -------------------------------------------------------------------------------- /readMe.md: -------------------------------------------------------------------------------- 1 | # Single-Pixel Hyperspectral Imaging (SPIHIM) Datasets 2 | 3 | # Version 1.0 and later 4 | 5 | Our datasets are made publicly available following FAIR (findability, accessibility, interoperability, reusability) principles through the pilot warehouse 6 | * https://pilot-warehouse.creatis.insa-lyon.fr/#collection/6140ba6929e3fc10d47dbe3e 7 | 8 | *Reference:* 9 | * G. Beneti-Martin, L Mahieu-Williame, T Baudier, N Ducros, "OpenSpyrit: an Ecosystem for Reproducible Single-Pixel Hyperspectral Imaging," Optics Express, Vol. 31, No. 10, (2023). [DOI](https://doi.org/10.1364/OE.483937). [Main PDF](https://hal.science/hal-03910077). [Supplemental document](https://hal.science/hal-03910077v1/file/revised%20%281%29.pdf). 10 | 11 | # Version 0 12 | 13 | *License:* The SPIHIM datasets are distributed under the Creative Commons Attribution 4.0 International license ([CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)) 14 | 15 | *Reference:* Please reference this work 16 | * A Lorente Mur, B Montcel, F Peyrin, N Ducros. Deep neural networks for single-pixel compressive video reconstruction. SPIE Photonics Europe, Proc. vol. 11351, Unconventional Optical Imaging II, pp.27, Apr 2020, France. [⟨hal-02547800⟩](https://hal.archives-ouvertes.fr/hal-02547800/) 17 | 18 | *Contact:* nicolas.ducros@insa-lyon.fr, CREATIS Laboratory, University of Lyon, France. 19 | 20 | ## Setup Description 21 | ### Hardware 22 | The set-up is depicted below and in detail [[ALM2020](https://hal.archives-ouvertes.fr/hal-02547800/document)]. The telecentric lens (TL; Edmund Optics 62901) is positioned such that its image side projects the image of the sample (S) onto the digital micro-mirror device (DMD; vialux V-7001), which is positioned at the object side of the lens. The object is transparent and is illuminated by a LED lamp (L; Thorlabs LIUCWHA/M00441662). The DMD can implement different light patterns by reflection of the incident light onto a relay lens (RL), which projects the light into an optical fiber (OF; Thorlabs FT1500UMT 0.39NA). This optical fiber is connected to a compact spectrometer (SM; BWTek examplar BRC115P-V-ST1). A filter wheel (FW) containing neutral optical densities is placed behind the lamp to reduce the light flux 23 | 24 | ![](setup.jpg) 25 | ### Mathematical Model 26 | The setup acquires M = α HF where F in **R** n x λ represents the sample hypercube, H in **R** m x n the measurement matrix, and α is a multiplicative factor that depends on and the optical density. The measurement matrix contains the patterns that are uploaded onto the DMD. Here, m represents the number of patterns, n the number of pixels of the patterns, and λ the number of spectral bins. 27 | 28 | The multiplicative factor is given by α = φ 10-OD Δt (in photons), where φ (in photons/s) represents the given light flux, OD is neutral optical density and Δt is the integration time. 29 | 30 | ### Hadamard Acquisitions 31 | We acquire Hadamard patterns that we split into positive and negative parts, which are concatenated in the measurement matrix. Precisely, Hm contains the positive parts and Hm+1 the negative parts, such that Hm - Hm+1 is a Hadamard pattern. 32 | 33 | We sequentially upload onto the DMD all of the m = 2 x 4096 Hadamard (split) patterns of dimension n = 64 x 64 pixels. The patterns in PNG format can be downloaded [here](https://www.creatis.insa-lyon.fr/~ducros/Spihim/Hadamard_64x64.zip). 34 | 35 | We acquire different datasets for the same object by selecting different neutral densities OD and different integration times Δt. 36 | 37 | ## Summary of the SPIHIM datasets 38 | The following datasets are provided 39 | * 04-Feb-2020 session ([description](#04-Feb-2020-session)). [Download zip](https://www.creatis.insa-lyon.fr/~ducros/Spihim/spihim_2020-Feb-04.zip). 40 | * 11-Jun-2020 session ([description](#11-Jun-2020-session)). [Download zip](https://www.creatis.insa-lyon.fr/~ducros/Spihim/spihim_2020-Jun-11.zip). 41 | * 01-Jul-2020 session ([description](#01-Jul-2020-session)). [Download zip](https://www.creatis.insa-lyon.fr/~ducros/Spihim/spihim_2020-Jul-01.zip) 42 | * 18-Nov-2020 session ([description](#18-Nov-2020-session)). [Download zip](https://www.creatis.insa-lyon.fr/~ducros/Spihim/spihim_2020-Nov-18.zip) 43 | * All datasets compatible with the SPYRIT deep reconstruction networks (e.g., [Comp-Net](https://github.com/openspyrit/spyrit-examples/tree/master/2021_Optics_express)). [Download zip](https://www.creatis.insa-lyon.fr/~ducros/Spihim/spihim_2020_nets.zip) 44 | 45 | We provide the description of each measurement session in the sections below. 46 | 47 | ## Data Reading, measurement matrix, and reconstruction 48 | 49 | Based on [SPIRiT](https://github.com/nducros/SPIRIT), we provide matlab scripts that 50 | 51 | * read, reconstruct and plot the datasets (see `./scripts/read_*_.m`) 52 | * build the forward operator H that maps the image of the sample the onto the measured Hadamard coefficients (see `./scripts/build_forward_operator.m`) 53 | 54 | The (full) Hadamard matrix H in **R** n x n can be downloaded [here](https://www.creatis.insa-lyon.fr/~ducros/Spihim/Hadamard_64x64_forward_stl10_unlabeled.mat). It applies to a measurement vector in **R** n, where the missing coefficients have been field with zeros. To compute the (reduced) measurement matrix H in **R** m x n, see `./scripts/build_forward_operator.m`. 55 | 56 | The acquisition is such that the patterns with maximum variance are acquired first. We provide [here](https://www.creatis.insa-lyon.fr/~ducros/Spihim/Hadamard_64x64_cov_stl10_unlabeled.mat) the covariance matrix used to defined the acquisition order ; it was computed on the [STL-10](https://ai.stanford.edu/~acoates/stl10/) image dataset. 57 | 58 | ## Description of the SPIHIM datasets 59 | #### 04-Feb-2020 session 60 | We acquired four samples: 61 | * A [star sector target](https://www.thorlabs.com/thorproduct.cfm?partnumber=R1L1S2P) printed on a paper sheet in black an white 62 | * A [star sector target](https://www.thorlabs.com/thorproduct.cfm?partnumber=R1L1S2P) printed on a paper sheet printed in color according to a Hue color wheel 63 | * A paper sheet with no printing 64 | * No object, i.e., the illumination LED lamp directly 65 | 66 | Filename | M | Δt (ms) | Comment | 67 | |--|--:|--:|--| 68 | SiemensBW1a_raw.mat | 408 | 4 | black and white star sector | 69 | SiemensColor8a_raw.mat | 408 | 32 | color star sector | 70 | SiemensColor8b_raw.mat | 1228 | 32 | color star sector | 71 | mydata_raw.mat | 512 | 4 | | 72 | paperSheet1b_raw.mat | 1228 | 4 | paper sheet | 73 | paperSheet2b_raw.mat | 1228 | 8 | paper sheet | 74 | paperSheet4b_raw.mat | 1228 | 16 | paper sheet | 75 | siemensStar1a_raw.mat | 408 | 4 | black and white star sector | 76 | siemensStar1b_raw.mat | 1228 | 4 | black and white star sector | 77 | siemensStar2a_raw.mat | 408 | 8 | black and white star sector | 78 | siemensStar2b_raw.mat | 1228 | 8 | black and white star sector | 79 | siemensStar4a_raw.mat | 408 | 16 | black and white star sector | 80 | siemensStar4b_raw.mat | 1228 | 16 | black and white star sector | 81 | siemensStar8a_raw.mat | 408 | 32 | black and white star sector | 82 | siemensStar8b_raw.mat | 1228 | 32 |black and white star sector | 83 | siemensStarColor1b_raw.mat | 1228 | 4 | color star sector | 84 | siemensStarColor2b_raw.mat | 1228 | 8 | color star sector | 85 | siemensStarColor4b_raw.mat | 1228 | 16 | color star sector | 86 | siemensStarColor4c_raw.mat | 2456 | 16 | color star sector | 87 | siemensStarColor8d_raw.mat | 4096 | 32 | color star sector | 88 | whiteLED1a_raw.mat | 408 | 4 | no object | 89 | 90 | 91 | #### 11-Jun-2020 session 92 | First, we acquired the LED source directly with no sample 93 | 94 | Filename | Δt (ms) | OD (-)| Comment | 95 | |--|--:|--:|--| 96 | noObjectD_1_0.0_raw.mat | 4 | 0.0 | Saturate 97 | noObjectD_1_0.3_01_raw.mat | 4 | 0.3 | Repeat same measurement 4x to simulate longer Δt while avoiding saturation 98 | noObjectD_1_0.3_02_raw.mat | 4 | 0.3 | 99 | noObjectD_1_0.3_03_raw.mat | 4 | 0.3 | 100 | noObjectD_1_0.3_04_raw.mat | 4 | 0.3 | 101 | noObjectD_1_0.3_raw.mat | 4 | 0.3 | 102 | noObjectD_1_0.6_raw.mat | 4 | 0.6 | 103 | noObjectD_1_1.0_raw.mat | 4 | 1.0 | 104 | noObjectD_1_1.3_raw.mat | 4 | 1.3 | 105 | 106 | Second, we acquired the [star sector target](https://www.thorlabs.com/thorproduct.cfm?partnumber=R1L1S2P) 107 | 108 | Filename | Δt (ms) | OD (-)| Comment | 109 | |--|--:|--:|--| 110 | starSectorD_2_0.0_01_raw.mat | 8 | 0.0 | Repeat same measurement 4x to simulate longer Δt while avoiding saturation 111 | starSectorD_2_0.0_02_raw.mat | 8 | 0.0 | 112 | starSectorD_2_0.0_03_raw.mat | 8 | 0.0 | 113 | starSectorD_2_0.0_04_raw.mat | 8 | 0.0 | 114 | starSectorD_2_0.0_05_raw.mat | 8 | 0.0 | 115 | starSectorD_2_0.0_06_raw.mat | 8 | 0.0 | 116 | starSectorD_2_0.0_07_raw.mat | 8 | 0.0 | 117 | starSectorD_2_0.0_08_raw.mat | 8 | 0.0 | 118 | starSectorD_2_0.0_09_raw.mat | 8 | 0.0 | 119 | starSectorD_2_0.0_10_raw.mat | 8 | 0.0 | 120 | starSectorD_2_0.0_raw.mat | 8 | 0.0 | 121 | starSectorD_2_0.3_raw.mat | 8 | 0.3 | 122 | starSectorD_2_0.6_raw.mat | 8 | 0.6 | 123 | starSectorD_2_1.0_raw.mat | 8 | 1.0 | 124 | starSectorD_2_1.3_raw.mat | 8 | 1.3 | 125 | 126 | Third, we acquire the [star sector target](https://www.thorlabs.com/thorproduct.cfm?partnumber=R1L1S2P) that was off-centered 127 | 128 | Filename | Δt (ms) | OD (-)| Comment | 129 | |--|--:|--:|--| 130 | R1L1S2P_1_0.0_raw.mat | 4 | 0.0 | Shows the 'R1L1S2P' writing 131 | R1L1S2P_1_0.3_raw.mat | 4 | 0.3 | 132 | R1L1S2P_1_0.6_raw.mat | 4 | 0.6 | 133 | R1L1S2P_1_1.0_raw.mat | 4 | 1.0 | 134 | R1L1S2P_1_1.3_raw.mat | 4 | 1.3 | 135 | Thorlabs_1_0.0_raw.mat | 4 | 0.0 | Shows the 'THORLABS' writing 136 | Thorlabs_1_0.3_raw.mat | 4 | 0.3 | 137 | Thorlabs_1_0.6_raw.mat | 4 | 0.6 | 138 | 139 | Finally, we acquire images from the STL10 databaset 140 | 141 | Filename | Δt (ms) | OD (-)| Comment | 142 | |--|--:|--:|--| 143 | stl10_01_32ms_cp_raw.mat | 32 | 0.0 | horse displayed on my cell phone (cp) 144 | stl10_01_32ms_ps_raw.mat | 32 | 0.0 | horse printed on a paper sheet (ps) 145 | stl10_01_64ms_ps_raw.mat | 64 | 0.0 | horse printed on a paper sheet (ps) 146 | stl10_08_32ms_ps_raw.mat | 32 | 0.0 | bird printed on a paper sheet (ps) 147 | 148 | #### 01-Jul-2020 session 149 | First, we acquired the LED source directly with no sample. Note that the `noObjectF1` series and the `noObjectF2` series refer to two different positions of the LED lamp. 150 | 151 | Filename | Δt (ms) | OD (-)| Comment | 152 | |--|--:|--:|--| 153 | noObjectF1_1_0.3_01_raw.mat | 4 | 0.3 | Repeat same measurement 15x to simulate longer Δt while avoiding saturation 154 | noObjectF1_1_0.3_02_raw.mat | 4 | 0.3 | 155 | noObjectF1_1_0.3_03_raw.mat | 4 | 0.3 | 156 | noObjectF1_1_0.3_04_raw.mat | 4 | 0.3 | 157 | noObjectF1_1_0.3_05_raw.mat | 4 | 0.3 | 158 | noObjectF1_1_0.3_06_raw.mat | 4 | 0.3 | 159 | noObjectF1_1_0.3_07_raw.mat | 4 | 0.3 | 160 | noObjectF1_1_0.3_08_raw.mat | 4 | 0.3 | 161 | noObjectF1_1_0.3_09_raw.mat | 4 | 0.3 | 162 | noObjectF1_1_0.3_10_raw.mat | 4 | 0.3 | 163 | noObjectF1_1_0.3_11_raw.mat | 4 | 0.3 | 164 | noObjectF1_1_0.3_12_raw.mat | 4 | 0.3 | 165 | noObjectF1_1_0.3_13_raw.mat | 4 | 0.3 | 166 | noObjectF1_1_0.3_14_raw.mat | 4 | 0.3 | 167 | noObjectF1_1_0.3_15_raw.mat | 4 | 0.3 | 168 | noObjectF1_1_0.3_raw.mat | 4 | 0.3 | 169 | noObjectF1_1_0.6_raw.mat | 4 | 0.6 | 170 | noObjectF2_1_0.3_01_raw.mat | 4 | 0.3 | Not the same LED lamp position as in the `noObjectF1` series. We repeat same measurement 9x to simulate longer Δt while avoiding saturation 171 | noObjectF2_1_0.3_02_raw.mat | 4 | 0.3 | 172 | noObjectF2_1_0.3_03_raw.mat | 4 | 0.3 | 173 | noObjectF2_1_0.3_04_raw.mat | 4 | 0.3 | 174 | noObjectF2_1_0.3_05_raw.mat | 4 | 0.3 | 175 | noObjectF2_1_0.3_06_raw.mat | 4 | 0.3 | 176 | noObjectF2_1_0.3_07_raw.mat | 4 | 0.3 | 177 | noObjectF2_1_0.3_08_raw.mat | 4 | 0.3 | 178 | noObjectF2_1_0.3_09_raw.mat | 4 | 0.3 | 179 | noObjectF2_1_0.3_raw.mat | 4 | 0.3 | Not the same LED lamp position as in the `noObjectF1` series 180 | noObjectF2_1_0.6_raw.mat | 4 | 0.6 | 181 | noObjectF2_1_1.0_raw.mat | 4 | 1.0 | 182 | noObjectF2_1_1.3_raw.mat | 4 | 1.3 | 183 | 184 | Filename | Δt (ms) | OD (-)| Comment | 185 | |--|--:|--:|--| 186 | stl10_03_1_0.0_raw.mat | | | 187 | stl10_05_1.5_0.0_01_raw.mat | | | 188 | stl10_05_1.5_0.0_02_raw.mat | | | 189 | stl10_05_1.5_0.0_03_raw.mat | | | 190 | stl10_05_1.5_0.0_04_raw.mat | | | 191 | stl10_05_1.5_0.0_05_raw.mat | | | 192 | stl10_05_1.5_0.0_06_raw.mat | | | 193 | stl10_05_1_0.0_raw.mat | | | 194 | stl10_05_1_0.3_raw.mat | | | 195 | stl10_05_1_0.6_raw.mat | | | 196 | 197 | #### 18-Nov-2020 session 198 | Contrary to previous datasets, the DMD frequency is set to 1 kHz and the DMD patterns are 4-bit (see `par.DMD_fr` and `par.bitplane`). 199 | 200 | Filename | M | Δt (ms) | Comment | 201 | |--|--:|--:|--| 202 | noObject_01ms_raw.mat | 8192 | 1 | | 203 | noObject_10ms_raw.mat | 8192 | 10 | | 204 | starSector_01ms_raw.mat | 8192 | 1 | Starsector in the image plane | 205 | starSector_variableFilter_C_10ms_raw.mat | 8192 | 10 | Starsector in the image plane, filter between lamp and image plane | 206 | variableFilter_01ms_raw.mat | 8192 | 1 | Filter in the image plane | 207 | variableFilter_10ms_raw.mat | 8192 | 10 | Filter in the image plane| 208 | variableFilter_B_01ms_raw.mat | 8192 | 1 | Filter in the image plane | 209 | variableFilter_B_10ms_raw.mat | 8192 | 10 | Filter in the image plane | 210 | variableFilter_C_10ms_raw.mat | 8192 | 10 | Filter in the image plane `|` 211 | --------------------------------------------------------------------------------