-
Notifications
You must be signed in to change notification settings - Fork 0
satya415/example-based-super-resolution
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
====================NOTES====================== The example images included in this project are of sizes between 40-60 pixels in each dimension so it is not the best display of the effects of this algorithm (though you can still make out that high frequency details are being added to the image, and it will appear less blurry). This algorithm is very costly, since it will essentially generate two sets of patches for data and these are sets are roughly 25x and 49x the size of the image (25 pixel and 49 pixel patch generated for each original pixel). Furthermore, it will build a K-D Tree out of these patches, and perform a search through a combination of those sets for 1/16 of the pixels in the final output image. It takes about .5-1.5 minutes to generate the training set for 5 images between 100-200 pixels in each dimension, and about 3-4 minutes to perform the super resolution for an image between 100-200 pixels in each dimension (the searching for the nearest neighbor to find the best match is the largest bottleneck) ==================GUI USAGE==================== To run the GUI, in Matlab, open the SuperResolution.m file, and just hit run Select the image to digitally enhance with superresolution. Select the training images. Generate the training set. Click to super-resify. =========Matlab Command Window Usage=========== %specify alpha, name for training data file, and input image files alpha = .5444 saveFileName = 'name.mat' inputImageFileNames = {'input1.png' 'input2.png' 'input3.png'} %generate traning set, will print statements indicating progress buildTrainingSet(inputImageFileNames, saveFileName, alpha); %will perform the actual super resolution, stores results in an output directory %that is created by MATLAB (output-images) superRes(saveFileName, 'imageToSuperRes.png', alpha); ==================================== [done] For Faces: ------------- inputImageFileNames = {'face_bill.png' 'face_generic1.png' 'face_generic2.png' 'face_jobs.png'}; buildTrainingSet(inputImageFileNames, saveFileName, alpha); superRes(saveFileName, 'face_ObamaSuperRes.png', alpha); ==================================== [done] For Cartoon: ------------- inputImageFileNames = {'cartoon_avatar1.png' 'cartoon_familyguy2.png' 'cartoon_simpsons1.png' 'cartoon_southpark1.png'}; buildTrainingSet(inputImageFileNames, saveFileName, alpha); superRes(saveFileName, 'cartoon_SuperResCartoon.png', alpha); ==================================== [done] For flowers: ------------- inputImageFileNames = { 'flowers1.png' 'flowers2.png' 'flowers3.png' 'flowers4.png' 'flowers5.png' }; buildTrainingSet(inputImageFileNames, saveFileName, alpha); superRes(saveFileName, 'flowersSuperRes.png', alpha); ==================================== Bad data: ------------- inputImageFileNames = { 'magnacarta.png' }; buildTrainingSet(inputImageFileNames, saveFileName, alpha); superRes(saveFileName, 'obama.png', alpha);
About
Automatically exported from code.google.com/p/example-based-super-resolution
Resources
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published