Your SlideShare is downloading. ×
Matlab Image Restoration Techniques
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Saving this for later?

Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime - even offline.

Text the download link to your phone

Standard text messaging rates apply

Matlab Image Restoration Techniques

3,323
views

Published on

Matlab Image Restoration Techniques

Matlab Image Restoration Techniques

Published in: Technology, Sports

0 Comments
3 Likes
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
3,323
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
3
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Matlab:Image Restoration Techniques
  • 2. Removing Noise By Linear Filtering
    Linear filters, such as averaging or Gaussian filters can be used to remove certain types of noise. An averaging filter is useful for removing grain noise from a photograph. Because each pixel gets set to the average of the pixels in its neighborhood, local variations caused by grain are reduced.
  • 3. Removing Noise By Median Filtering
    With median filtering, the value of an output pixel is determined by the median of the neighborhood pixels, rather than the mean. The median is much less sensitive than the mean to extreme values (called outliers). Median filtering is therefore better able to remove these outliers without reducing the sharpness of the image.
  • 4. Applying the averaging filter
    >>I=imread('img.bmp');
    >> I=I(:,:,1);
    >> imshow(I);
    >>K = filter2(fspecial('average',3),I)/255;
    >>figure, imshow(K)
  • 5. Applying the median filter
    >>I=imread('img.bmp');
    >> I=I(:,:,1);
    >> imshow(I);
    >> L = medfilt2(I,[3 3]);
    >>figure, imshow(L)
  • 6. Rectifying background illumination
    Step 1: Read Image
    Step 2: Use Morphological Opening to Estimate the Background
    Step 3: View the Background Approximation as a Surface
    Step 4: Subtract the Background Image from the Original Image
  • 7. Rectifying background illumination
    Step1: Read Image
    I = imread('rice.png'); imshow(I)
  • 8. Rectifying background illumination
    Step 2: Use Morphological Opening to Estimate the Background
    >>background = imopen(I,strel('disk',15));
    >>figure, surf(double(background(1:8:end,1:8:end))),zlim([0 255]); set(gca,'ydir','reverse');
    • Step 3: View the Background Approximation as a Surface
  • Rectifying background illumination
    Step 2: Use Morphological Opening to Estimate the Background
    • Step 3: View the Background Approximation as a Surface
  • Rectifying background illumination
    Step 4: Subtract the Background Image from the Original Image
    I2 = I - background; imshow(I2)