Face Hallucination

PCA Feature Reduction for Face Hallucination

Coefficients Matrix

Apply PCA to low resolution input image


im_path = 'SplitFrame_000.bmp';
lowres = imread(im_path);
dbl_lowres = double(lowres);
width = size(lowres,1);
height = size(lowres,2);

rshp_lowres = reshape(lowres, width*height, 3);

%% figure, imshow(rshp_lowres);
coeff = pca(double(rshp_lowres));


Multiply each low resolution training image by the coefficient matrix. The inner product is interpreted as projecting the training set into the coordinate space that provides maximum between class variance.

lowres_dir_path = uigetdir;
image_files = dir(strcat(lowres_dir_path, '\*.bmp'));
num_files = length(image_files);
table_of_features = [];
for i = 1 : num_files
    %% check isDir
    file_name = image_files(i).name;
    training_image = imread(strcat(lowres_dir_path ,'\', file_name));
    training_image = double(training_image);
    training_width = size(training_image, 1);
    training_height = size(training_image, 2);
    reshaped_training_image = reshape(training_image, width * height, 3);
    reduced_image = reshaped_training_image * coeff;
    table_of_features = [table_of_features; transpose(reduced_image)];


Finally save the results for KNN processing

save features.dat table_of_features -ascii -double;


[1] http://www.mathworks.com/matlabcentral/answers/167938-how-pca-can-be-applied-to-an-image-to-reduce-its-dimensionality-with-example


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