Anisotropic diffusion for a fingerprint image: resolution issues.

Question

Jay Ghosh 2022-8-7

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1775235-anisotropic-diffusion-for-a-fingerprint-image-resolution-issues

编辑： Jay Ghosh 2023-6-25

Hello,

I have been trying to understand how to successfully use matlab in-built anisotropic diffusion function (imdiffusefilt). To test the function I chose a fingerprint image (that has some noise and some gaps) as my input image. The fingerprint image (.gif) was dowloaded from this url. Incase the hyperlink does not work here is the link: https://www.mia.uni-saarland.de/weickert/demos.html.

There are two images (on 2nd row) under the tittle Gap completion by coherence-enhancing anisotropic diffusion. I will upload the images (1st one is my input image, and 2nd one is the desired result that I am unable to achieve) here as well.

This is the input image (gif):

This is the desired output:

I tried to run 'imdiffusefilt' with default setting, and then ran it with estimated parameters (using 'imdiffuseest' function). My result for the latter is worse. I am going to upload my code below. Any tip/advice on this would be great. Thanks everyone, in advance!

Here is my code:

close all
clear all
%% Read image file 
img = imread('finger2.gif');
%% Calculate diffusion parameters (to be used in imdiffusefilt below)
[gradThresh,numIter] = imdiffuseest(img);
%% Check pixel value frequencies (histogram)
figure(11)
histogram(img,805)
%% Apply anisotropic diffusion (imdiffusefilt)
img_diffusion = imdiffusefilt(img,'NumberOfIterations',5);           % using default parameters
%% Anisotropic diffusion (using estimated parameters) 
% img_diffusion = imdiffusefilt(img,'connectivity','maximal', ...
%     'ConductionMethod','exponential', 'GradientThreshold',gradThresh, ...
%     'NumberOfIterations',numIter);
%% Display the results (original vs anisotropic diffustion)
figure(1)
montage({img,img_diffusion},'ThumbnailSize',[1000,700])
title(['  Original Imgae (Left)        vs.          ' ...
    'Smoothing w/ Anisotropic Diffusion (Right)'])

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Answer 1

Bjorn Gustavsson 2022-8-8

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1775235-anisotropic-diffusion-for-a-fingerprint-image-resolution-issues#answer_1022780

You might find that you have more control over the anisotropic diffusion with the cedif or eedif functions from the nonlinear-diffusion-toolbox. You might have to doodle around to find the best selection of input-parameters and combination of ordinary filtering and nonlinear isotropic and anisotropic filtering to suit your case. Definitely have a look at that toolbox and its demos and documentation.

HTH

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Jay Ghosh 2022-8-19

在 MATLAB Online 中打开

Hi Bjorn, I have been able to explore the diffusion toolbox a little during the past few days. The results look decent and are a lot better than anything I had before (see the images below). However, the final output image is not as good as the one I attached with my 1st message (see above); there are rooms for improvement (see the green rectangle on the image below: this area is not resolved).

To elaborate, I just used cedif function (my script w/ parameter values is attached below). Could you please take a look at my scripts and resulting images and let me know your comments / any-further-suggestions? I would greatly appreciate that. Thank you so much!

close all
clear all
%% CEDIF: Coherence enhancing diffusion
%% Read image file
img = imread('finger2.gif');             % fingerprint image
%% Apply cedif to input image
% Set parameters
lambda = 4; sigma = 1.2; rho = 7; m = 100; stepsize = 0.2; steps = 10;
% Apply cedif
img_cedif = cedif(img, lambda, sigma, rho, m, stepsize, steps, 1, 5, ...
    'dfstep',2,'imscale', 'grad', 'struc');
%% Convert doubles to uint8
img_cedif = uint8(255*mat2gray(img_cedif));
%% Display the results (original vs anisotropic diffustion)
figure(1)
montage({img,img_cedif},'ThumbnailSize',[1000,700])
title(['   Original Imgae (Left)             |           ' ...
    'CEDIF: Coherence Enhanced Diffusion (Right)'])

nldif:

I am now trying to explore nldif function to see if I can achieve further improvements (see attached script below), but no luck so far with resolving the center-right part (see the image below: the area inside the red rectangle), though I could see that some noises have been removed.

close all 
clear all
%% Read image file
img = imread('finger2.gif');             % fingerprint image
%% Apply non-linear diffusion
% Set parameters 
lambda = 4; sigma = 1.2; rho = 7; m = 100; stepsize = 0.2; steps = 10;
% Or:
% s = 7;
% lambda = [linspace(4,4,s)];   
% sigma = [linspace(1.2,1.2,s)]; 
% m = 100; 
% stepsize = 0.2; steps = s;
y = nldif(img, lambda, sigma, m, stepsize, steps, ...
    'dfstep', 2, 'grad', 'aos', 'imscale', 1,3);   % what are (1,3)? => find out
img_nldif = uint8(255*mat2gray(y));
%% Display the results (original vs anisotropic diffustion)
figure(2)
montage({img,img_nldif},'ThumbnailSize',[1000,700])
title(['   Original Imgae (Left)             |           ' ...
    'nldif: Non-linear Diffusion (Right)'])

Jay Ghosh 2022-8-29

编辑：Jay Ghosh 2022-8-29

Thanks, Bjorn! Yes, I am still on it, spending some time everyday:)

Another interesting observation is the follwoing. I have been applyig cedif to some image samples that are more realistic, in the sense that those are the kind of images that I will be likely using for my research. These sample images represent magnetic (field) anomaly data (see attached *.png input files).

Once I convert them to grayscale (input for cedif), and run, say, using 10 timestepts, the images begin to shrink from all 4 directions w/ every timestep iteration. For some images, there are zero value blocks inside the image as well (see attached output images: img1.png, img11.png, img2.png, img21.png). It is evident from gradient magnitude plots/images as well (img1_grad_mag.png, img2_grad_mag.png). I suppose we are getting these zero-valued-blocks-of-pixels in the output images as 'cedif' calculates the derivatives. But, I am not sure how to fix this, yet.

I will continue to explore. However, I wanted to share these new findings as well (for these new images).

Thank you very much, indeed, for taking the time out of your busy scedule. I sincerely appreciate it.

Jay Ghosh 2022-9-3

编辑：Jay Ghosh 2022-9-3

090222-Anomaly_NE_Pacific.png.png

Bjorn, I started over just to make sure that I am doing everything correctly and the dimensions are correct. So, now I have the rgb input image of dimenion [630 840 3], which is called '090222-Anomaly_NE_Pacific.png' (attached above).

After converting it to grayscale mage, dimension become [630 840].

Next, I checked for NaN values, and there is none. If I do not have any NaNs, then why do I still get this yellow/white region, when I run cedif? I am not sure.

My code is attached below:

close all

clear all

%% CEDIF: Coherence enhancing diffusion

%% Read image file

img = rgb2gray(imread('090222-Anomaly_NE_Pacific.png'));

sz = size(img);

any(isnan(img(:)));

% Set parameters (NE Pacific) [see parameter descriptions under cedifdemo]

lambda = 10; sigma = 1.2; rho = 2; m = 10; stepsize = .5; steps = 10;

img_cedif = cedif(img, lambda, sigma, rho, m, stepsize, steps, 1, 5, ...

'dfstep',2, 'grad', 'struc','gamma',0.01); % 'imscale'

%% Convert doubles to uint8

img_cedif2 = uint8(255*mat2gray(img_cedif));

figure(22)

montage({img,img_cedif2},'ThumbnailSize',[1000,700])

title([' Original Image (Left) | ' ...

'CEDIF: Coherence Enhanced Diffusion (Right)'])

Walter Roberson 2022-9-7

在 MATLAB Online 中打开

imwrite(rescale(flipud(zm), 'InputMin', -1000, 'InputMax', 1500),'test2.png');

Jay Ghosh 2023-6-25

编辑：Jay Ghosh 2023-6-25

@Bjorn Gustavsson, I have been finally able to get back to running difusion for part of my research. Now, I have been running diffusion on original data, instead of on an image file (your advise was to do the analysis on the data).

The functions I have been using are 'cedif' and 'CorerenceFilter' (both from file exchange). For both the functions, I have noticed that the diffusion results are not the same as it was for an image file (0-255) as an input. For original data (range:(-290,273)) as an input, I have to run much longer diffusion timesteps to get similar results, but also diffusion output just does not look as sharp (in terms of preserving edges) as it did before. For an input image file, for 'CoherenceFilter', only a timestep of 20 (T=20) produced some good diffusion output, however, for input data (-290,273), it requres more timesteps to see detectable diffusion, and results are a lot more blurry than it was for an image input.

I was wondering if it has anything to do with the fact that data file has negative values (w/ mean being close to zero), and/or the fact that data file has longer range (-290,273) compared to an image file (0-255). The function description, for both cedif and CoherenceFilter says that input is an image file.

Would you have any thoughts on this? Thank you very much!

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