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Code and data for the research paper "A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement" (Submitted to IEEE Transactions on Cybernetics)
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Code for our paper "A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement"
- The code for the comparison method is also provided, seelowlight
- Downloads:google Drive (Just unzip data to current folder)
- Datasets
VV, LIME, NPE, NPE-ex1, NPE-ex2, NPE-ex3, DICM, MEF - Since some methods are quite time-consuming, we also provide their results (e.g.
results__dong@VV.zip) - Since some metrics are quite time-consuming, we also provide their results (
TestReport.zip)
- Datasets
- All the experiments can be reproduced easily by running
experiments.m
From left to right: input images, results of MSRCR, Dong, NPE, LIME, MF, SRIE, and BIMEF(ours).
- VV （**Busting image enhancement and tone-mapping algorithms: **A collection of the most challenging cases）
- LIME-data
- NPE-data, NPE-ex1, NPE-ex2, NPE-ex3
- DICM —— 69 captured images from commercial digital cameras:Download (15.3 MB)
- MEFdataset
- Original code is tested onMatlab 2016b 64bit, Windows 10.
- matlabPyrTools is required to run VIF metric (
vif.m).
Runstartup.m to add required path, then you are able to try the following demo.
I= imread('yellowlily.jpg');J= BIMEF(I);subplot121; imshow(I); title('Original Image');subplot122; imshow(J); title('Enhanced Result');
ReplaceBIMEF with other methods you want to test.
.├── data # put your datasets here│ ├── MEF # dataset name (VV, LIME, NPE...)│ ├── out │ │ ├── loe100x100 # LOE visualization results│ │ ├── TestReport.csv # results of metrics│ │ ├── TestReport__xxxx.csv # backups of TestReport│ │ └── xxx__method.PNG # output images│ └── xxx.jpg # input images│├── lowlight # lowlight image enhancement methods├── quality # image quality metrics (blind or full-reference)├── util # provide commonly used utility functions│├── demo.m # simple demo of lowlight enhancement├── experiments.m # reproduce our experiments└── startup.m # for installationRun experiments.
% specify datasetsdataset= {'VV''LIME''NPE''NPE-ex1''NPE-ex2''NPE-ex3''MEF''DICM'};dataset= strcat('data',filesep,dataset,filesep,'*.*');% specify methods and metricsmethod= {@multiscaleRetinex @dong @npe @lime @mf @srie @BIMEF};metric= {@loe100x100 @vif};for d=dataset, data= d{1};data, Test= TestImage(data); Test.Method=method; Test.Metric=metric;% run test and display resultsTest,% save test to a .csv file save(Test);end
Show test reports.
% tableTestReport('TestReport__VV.csv'),% boxplotTestReport('TestReport__MEF.csv').boxplot;
Our method (BIMEF) has the lowest LOE and the highest VIF.
