# applyfilterfftsameLocalNorm

## PURPOSE

applyfilterfftsameLocalNorm - Filter image by a bank of filters with local normalization.

## SYNOPSIS

function allFiltered = applyfilterfftsameLocalNorm(I, allfilter, localRadiusProportion)

## DESCRIPTION

     applyfilterfftsameLocalNorm - Filter image by a bank of filters with local normalization.

     I: input images
     allfilter: filter bank
     threshold: filter response is set to zero if below threshold

## CROSS-REFERENCE INFORMATION

This function calls:

This function is called by:

- applyfilterMultiScale applyfilterMultiScale - Filter image by a bank of multiscale filters, with local normalization.

## SOURCE CODE

    0001 function allFiltered = applyfilterfftsameLocalNorm(I, allfilter, localRadiusProportion)
    0002 % applyfilterfftsameLocalNorm - Filter image by a bank of filters with local normalization.
    0003 %
    0004 % I: input images
    0005 % allfilter: filter bank
    0006 % threshold: filter response is set to zero if below threshold
    0007
    0008 numImage = size(I, 2);    % number of training images
    0009 numOrient = length(allfilter);     % number of orientations
    0010 allfilter = reshape(allfilter,[1 numOrient]);
    0011 h = (size(allfilter{1}, 1)-1)/2;  % half size of filters
    0012 [sx, sy] = size(I{1});  % size of images
    0013 thresholdFactor = 0.01;
    0014
    0015 allFiltered = cell(numImage, numOrient);  % filtered images
    0016 for i = 1 : numImage
    0017     fftI{i} = fft2(I{i}, sx+h+h, sy+h+h);
    0018 end
    0019 for o = 1 : numOrient
    0020    fftf{o} = fft2(allfilter{1, o}, sx+h+h, sy+h+h);
    0021 end
    0022
    0023 radius = floor((2*h+1)*localRadiusProportion);
    0024 radius = max(20*2+1,radius);
    0025 localHalfx = radius; localHalfy = radius;
    0026 for i = 1 : numImage
    0027    tot = 0.;
    0028    fftIi = fftI{i};
    0029    for o = 1 : numOrient
    0030       fftfo = fftf{o};
    0031       out = ifft2(fftIi.*fftfo);
    0032       filtered = out(h+1:h+sx, h+1:h+sy);
    0033       re = real(filtered); im = imag(filtered);
    0034       energy = re.*re + im.*im;  % compute the local energy
    0035       energy([1:h sx-h:sx],:) = 0.; energy(:,[1:h sy-h:sy]) = 0.;
    0036       allFiltered{i, o} = energy;
    0037       tot = tot + sum(sum(energy((h+1):(sx-h-1), (h+1):(sy-h-1))))/(sx-2*h-1)/(sy-2*h-1);
    0038    end
    0039    ave = tot/numOrient;
    0040    for o = 1 : numOrient
    0041        allFiltered{i, o} = single(allFiltered{i, o}/ave);  % normalizing by whitening
    0042    end
    0043        ClocalNormalizeSingle(sx, sy, numOrient, h, localHalfx, localHalfy, allFiltered(i, :), thresholdFactor);
    0044 end
    0045