Skip to main content

Drift correction in Matlab

In order to improve S/N ratio, microscopist uses several short acquisition time images, and then sum them up. So the drift correction is very important. Here is the demo of how to use Matlab do drift correction.

In the first, load an image, and using circshift function to shift the object in the image.
Then use fft cross correlation to compute the moving distance.
Finally, shift the object to the origial position.

Here is the testing code:
==
% Demo of drift correction
% 2018/11/15  by Renfong

im1= imread('cameraman.tif');    % reference image
[sy,sx]=size(im1);

% shift the object
im2=circshift(im1,[20,10]);    % the object moved down 20 pixels and moved right 10 pixels.
figure(1);
subplot(121);imshow(im1);
subplot(122);imshow(im2);

% Using fft cross correlations to detect the moving distance[1]
fftim1=fft2(im1);
fftim2=fft2(im2);
cc=fftshift(ifft2(fftim1.*conj(fftim2)));
[shiftY,shiftX]=find(cc==max(cc(:)));
shiftY=shiftY-fix(sy/2)-1;
shiftX=shiftX-fix(sx/2)-1;
figure(2);
imshow(mat2gray(cc)); hold on;
plot(fix(sx/2),fix(sy/2),'r+'); hold off;



% show result
corrected_im2=circshift(im2,[shiftY,shiftX]);
figure(3);
subplot(121);imshow(im2);
subplot(122);imshow(corrected_im2);
==

Ref:
[1]  doi: 10.1017/S1551929514000790

Comments

Popular posts from this blog

Top hat filter

The top_hat filter can be used to detect the relatively small edges/peaks superimposed on large background signals. The concept came from the EELS workshop during IMC19. Thanks to Prof. Nestor J. Zaluzec. -- // Using Top_hat digital filter to detect the  relatively small edges  //    superimposed on large background signals. // // ref: Ultramicroscopy 18 (1985) 185-190  //      Digital Filters  for Application to Data Analysis in EELS //      by Nestor J. ZALUZEC // Parameters: // win_s: signal window (default:3) // win_b: background window (default:3) //  a_s : amplitude of signal (fixed value) //  a_b : amplitude of background  (fixed value) // Renfong 2018/10/11 // Main function image Top_Hat_Filter(image img, number win_s, number win_b) { // read image string fname=img.GetName() number sx,sy img.getsize(sx,sy) // filter image img2 := imageclone(img)*0 //the area between...

HyperSpy - read the calibration information in a dm3/dm4 file

Some example of dm3 file reading by using Python HyperSpy package, which can read the detail information of the dm file. -- # import packages import numpy as np import hyperspy.api as hs # load file sp=hs.load('sp.dm3') # Read the axis information      # Print all the calibration detail print(sp.axes_manager) ''' <Axes manager, axes: (272|2042)>             Name |   size |  index |  offset |   scale |  units  ================ | ======= | ====== | ======= | ======= | ======                     x |    272 |      0 |       -0 |  0.0025 |     µm   --------------- |  ------ | ----- |  ------ | ------- | ------    Energy loss |  2042 |         | 3.2e+02 |       1 |     eV...

MLLS in matlab

MLLS stands for  multiple linear least squares fitting, which is the common strategy for the solving EELS edge overlapping and which is also built-in the GMS software. The target spectrum Y and the reference spectrum X Y = A * X Assuming Y is 1*256 matrix and we have three reference spectrums, ie, X is 3*256 matrix. So A is 1*3 matrix. The target is to solve A. If Y and X are n*n matrices, we can use the simple formula Y * inv(X) = A * X * inv(X), ie., A = Y * inv(X). However, Y and X are not n*n  matrices, it is necessary to have some trick to solve it. We can multiply the transpose matrix to produce n*n matrix. Y * X' = A * X * X'  (ps X' means the transpose matrix of X) so A = Y * X' * inv(X * X') Here is the Matlab code: =========  % create target spectrum x=0:256; c=[90,120,155]; sig=[5,10,8]; int=[5,10,8]; xn=zeros(size(x)); ref=zeros(length(c),length(x)); factor=rand(size(c))'; for i=1:length(c)     xn=xn+int(i)*ex...