0
Upcoming SlideShare
×

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.
Standard text messaging rates apply

# Matlab Image Restoration Techniques

3,473

Published on

Matlab Image Restoration Techniques

Matlab Image Restoration Techniques

Published in: Technology, Sports
3 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total Views
3,473
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
0
0
Likes
3
Embeds 0
No embeds

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
&gt;&gt; I=I(:,:,1);
&gt;&gt; imshow(I);
&gt;&gt;K = filter2(fspecial(&apos;average&apos;,3),I)/255;
&gt;&gt;figure, imshow(K)
• 5. Applying the median filter
&gt;&gt; I=I(:,:,1);
&gt;&gt; imshow(I);
&gt;&gt; L = medfilt2(I,[3 3]);
&gt;&gt;figure, imshow(L)
• 6. Rectifying background illumination
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