Noise Removing of an image matlab Code

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Asif Hasan 2014-5-18

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https://ww2.mathworks.cn/matlabcentral/answers/130082-noise-removing-of-an-image-matlab-code

评论： Image Analyst 2014-8-29

Hi! Everyone i am new in matlab . I tried many process in matlab but can not get my desire output. Can anyone help me with some code that can remove image noise. The image is bellow. Please give me code to remove noise.. I need only white part and the other part will be black. Any help will be appreciated....... Thanks in advance...

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Asif Hasan 2014-5-19

在 MATLAB Online 中打开

Sorry sir, Its my first time so. And i got this figure using the adaptive clustering process the code is

function [lb,center] = adaptcluster_kmeans(im)

% This code is written to implement kmeans clustering for segmenting any % Gray or Color image. There is no requirement to mention the number of cluster for % clustering. % IM - is input image to be clustered. % LB - is labeled image (Clustered Image). % CENTER - is array of cluster centers. % Execution of this code is very fast. % It generates consistent output for same image.

if size(im,3)>1 [lb,center] = ColorClustering(im); % Check Image is Gray or not. else [lb,center] = GrayClustering(im); end

function [lb,center] = GrayClustering(gray) gray = double(gray); array = gray(:); % Copy value into an array. % distth = 25; i = 0;j=0; % Intialize iteration Counters. tic while(true) seed = mean(array); % Initialize seed Point. i = i+1; %Increment Counter for each iteration. while(true) j = j+1; % Initialize Counter for each iteration. dist = (sqrt((array-seed).^2)); % Find distance between Seed and Gray Value. distth = (sqrt(sum((array-seed).^2)/numel(array)));% Find bandwidth for Cluster Center. % distth = max(dist(:))/5; qualified = dist<distth;% Check values are in selected Bandwidth or not. newseed = mean(array(qualified));% Update mean.

        if isnan(newseed) % Check mean is not a NaN value.
            break;
        end
        if seed == newseed || j>10 % Condition for convergence and maximum iteration.
            j=0;
            array(qualified) = [];% Remove values which have assigned to a cluster.
            center(i) = newseed; % Store center of cluster.
            break;
        end
        seed = newseed;% Update seed.
    end
    if isempty(array) || i>10 % Check maximum number of clusters.
        i = 0; % Reset Counter.
        break;
    end

end toc

center = sort(center); % Sort Centers. newcenter = diff(center);% Find out Difference between two consecutive Centers. intercluster = (max(gray(:)/10));% Findout Minimum distance between two cluster Centers. center(newcenter<=intercluster)=[];% Discard Cluster centers less than distance.

% Make a clustered image using these centers.

vector = repmat(gray(:),[1,numel(center)]); % Replicate vector for parallel operation. centers = repmat(center,[numel(gray),1]);

distance = ((vector-centers).^2);% Find distance between center and pixel value. [~,lb] = min(distance,[],2);% Choose cluster index of minimum distance. lb = reshape(lb,size(gray));% Reshape the labelled index vector.

function [lb,center] = ColorClustering(im)

im = double(im); red = im(:,:,1); green = im(:,:,2); blue = im(:,:,3);

array = [red(:),green(:),blue(:)]; % distth = 25; i = 0;j=0; tic while(true)

    seed(1) = mean(array(:,1));
    seed(2) = mean(array(:,2));
    seed(3) = mean(array(:,3));
    i = i+1;
    while(true)
        j = j+1;
        seedvec = repmat(seed,[size(array,1),1]);
        dist = sum((sqrt((array-seedvec).^2)),2);
         distth = 0.25*max(dist);
        qualified = dist<distth;
        newred = array(:,1);
        newgreen = array(:,2);
        newblue = array(:,3);
        newseed(1) = mean(newred(qualified));
        newseed(2) = mean(newgreen(qualified));
        newseed(3) = mean(newblue(qualified));
        if isnan(newseed)
            break;
        end
        if (seed == newseed) | j>10
            j=0;
            array(qualified,:) = [];
            center(i,:) = newseed;
            %             center(2,i) = nnz(qualified);
            break;
        end
        seed = newseed;
    end
    if isempty(array) || i>10
        i = 0;
        break;
    end

end toc centers = sqrt(sum((center.^2),2)); [centers,idx]= sort(centers);

while(true) newcenter = diff(centers); intercluster =25; %(max(gray(:)/10)); a = (newcenter<=intercluster); % center(a,:)=[]; % centers = sqrt(sum((center.^2),2)); centers(a,:) = []; idx(a,:)=[]; % center(a,:)=0; if nnz(a)==0 break; end

end center1 = center; center =center1(idx,:); % [~,idxsort] = sort(centers) ; vecred = repmat(red(:),[1,size(center,1)]); vecgreen = repmat(green(:),[1,size(center,1)]); vecblue = repmat(blue(:),[1,size(center,1)]);

distred = (vecred - repmat(center(:,1)',[numel(red),1])).^2; distgreen = (vecgreen - repmat(center(:,2)',[numel(red),1])).^2; distblue = (vecblue - repmat(center(:,3)',[numel(red),1])).^2;

distance = sqrt(distred+distgreen+distblue); [~,label_vector] = min(distance,[],2); lb = reshape(label_vector,size(red)); %

and the color image is

I need to segment the sclera part from the image. I am doing sclera recognition project. If you have any matlab code for this please help. And thanks for reply.

Image Analyst 2014-5-19

Please just attach the m-file with the paper clip icon. Then read this http://www.mathworks.com/matlabcentral/answers/13205-tutorial-how-to-format-your-question-with-markup

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Answer 1

Image Analyst 2014-5-19

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/130082-noise-removing-of-an-image-matlab-code#answer_137342

Look here for algorithms : http://www.visionbib.com/bibliography/contents.html

Search for sclera: http://www.visionbib.com/bibliography_search.html

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Asif Hasan 2014-8-29

Thanks for the reply . But which one should i follow first for this kind of problem , there are many links. @Image Analist

Image Analyst 2014-8-29

Follow the ones that sound most promising. I have not read all the image processing papers in that narrow field, or even any of them, so I don't know which are any good. You'll have to figure that out on your own.

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