处理过的图像中的文物 [英] artifacts in processed images

问题描述

这个问题与我之前发布的 Matlab中的图像处理算法有关。 ，我已经得到了我想要的结果。

但现在我面临另一个问题，并在过程映像中获取一些文物。在我的原始图像（600张图像的堆栈）中，我看不到任何文物，请从指甲中看到原始图像：

但在我的10个处理结果中，我可以看到这些行：

/ p>

我真的不知道他们从哪里来？如果他们属于相机的传感器，为什么我不能看到他们在我的原始图像？任何想法？

编辑：

我已经添加了以下代码@Jonas。

 ％图像的平均值
 im = D {1} （：，:); 
 for i = 2：100 
 im = imadd（im，D {i}（:, :)）; 
 end 
 im = im / 100; 
 imshow（im，[]）; 
 
为i = 1：100 
 SD {i}（：，：）= imsubtract（D {i}（:, :)，im（:, :)）
结束
  

@belisarius要求更多图片，所以我要从手指上传4张图片（1280x1024）：

这里是黑色背景：

解决方案

这里是一个答案，意见将比上述方法更温和地删除线：

  im = imread（'image.png'）; ％原始图片
 imFiltered = im; ％过滤的图像将在这里结束
 imChanged = false（size（im））;％记录过滤器性能
 
％1）
％计算每列的分数在图像
％（其中列最清晰）的下部，计算直方图中每个
％bin的均值和标准差。 
 histograms = hist（double（im（501：520，:)），0：255）; 
 colMean =均值（直方图，2）; 
 colStd = std（直方图，0,2）; 
 
％2）
％现在循环虽然每个灰度高于零和... 
 greyLevel = 1：255 
 
％找到列其中灰度级像素的数量大于
％mean_n_graylevel + 3 * std_n_graylevel）。 - 这是包含
％统计许多像素与当前graylevel的列。 
 lineColumns = find（直方图（grayLevel + 1，:)）colMean（grayLevel + 1）+ 3 * colStd（grayLevel + 1））; 
 
现在删除原始图像中lineColumns中的所有灰度像素
 if（〜isempty（lineColumns））
 for col = lineColumns 
 imFiltered（：，col） = im（：，col）。* uint8（〜（im（：，col）== grayLevel））; 
 imChanged（：，col）= im（：col）== grayLevel; 
 end 
 end 
 end 
 
 imshow（imChanged）
 figure，imshow（imFiltered）
  pre> 
 
 
以下是过滤后的图片 
 
 
  
 
这显示了过滤器所影响的像素
 
 
  
 
This question is related to my previous post Image Processing Algorithm in Matlab in stackoverflow, which I already got the results that I wanted to. 

But now I am facing another problem, and getting some artefacts in the process images. In my original images (stack of 600 images) I can't see any artefacts, please see the original image from finger nail:



But in my 10 processed results I can see these lines:



I really don't know where they come from? 

Also if they belong to the camera's sensor why can't I see them in my original images? Any idea?

Edit:

I have added the following code suggested by @Jonas. It reduces the artefact, but does not completely remove them.
%averaging of images
im = D{1}(:,:);
for i = 2:100
 im = imadd(im,D{i}(:,:));
end
im = im/100;
imshow(im,[]);

for i=1:100
SD{i}(:,:)=imsubtract(D{i}(:,:),im(:,:))
end
@belisarius has asked for more images, so I am going to upload 4 images from my finger with speckle pattern and 4 images from black background size( 1280x1024 ):






And here is the black background:



解决方案
Here is an answer that in opinion will remove the lines more gently than the above mentioned methods:
im = imread('image.png');   % Original image
imFiltered = im;            % The filtered image will end up here
imChanged = false(size(im));% To document the filter performance 

% 1)
% Compute the histgrams for each column in the lower part of the image
% (where the columns are most clear) and compute the mean and std each
% bin in the histogram.
histograms = hist(double(im(501:520,:)),0:255);
colMean = mean(histograms,2);
colStd = std(histograms,0,2);

% 2)
% Now loop though each gray level above zero and...
for grayLevel = 1:255

    % Find the columns where the number of 'graylevel' pixels is larger than
    % mean_n_graylevel + 3*std_n_graylevel). - That is columns that contains
    % statistically 'many' pixel with the current 'graylevel'. 
    lineColumns = find(histograms(grayLevel+1,:)>colMean(grayLevel+1)+3*colStd(grayLevel+1));

    % Now remove all graylevel pixels in lineColumns in the original image
    if(~isempty(lineColumns))
        for col = lineColumns 
            imFiltered(:,col) = im(:,col).*uint8(~(im(:,col)==grayLevel));
            imChanged(:,col) = im(:,col)==grayLevel;
        end
    end 
end

imshow(imChanged)
figure,imshow(imFiltered)
Here is the image after filtering



And this shows the pixels affected by the filter



                        
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