Your SlideShare is downloading. ×
Image degradation and noise by Md.Naseem Ashraf
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

Image degradation and noise by Md.Naseem Ashraf

4,266
views

Published on

A small presentation on types of noise and image degradation in Digital Image Processing with small MATLAB examples.

A small presentation on types of noise and image degradation in Digital Image Processing with small MATLAB examples.

Published in: Education, Technology, Business

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

No Downloads
Views
Total Views
4,266
On Slideshare
0
From Embeds
0
Number of Embeds
9
Actions
Shares
0
Downloads
0
Comments
0
Likes
4
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. By Md.Naseem AshrafBE/5504/09IMAGE DEGRADATION&NOISEA Presentation for Digital Image ProcessingBirla Institute of Technology, Mesra, Extension Center - Patna
  • 2. What is Image Degradation?Image degradation is said to occur when acertain image under goes loss of storedinformation either due to digitization orconversion (i.e algortithmic operations),decreasing visiual quality.
  • 3. Image Degradation & RestorationModelThe initial image (source, f(x,y)) undergoes degradationdue to various operations, conversions and losses. Thisintroduces Noise. This Noisy Image is further restored viarestoration filters to make it visually acceptable for user.Degraded Image: = Degradation Function* Source + Noiseg(x,y) = h(x,y) * f(x,y) + n(x,y)
  • 4. What is Noise?Image noise is random (not present in the object imaged)variation of brightness or color information in images, andis usually an aspect of electronic noise. It can be producedby the sensor and circuitry of a scanner or digital camera.Image noise can also originate in film grain and in theunavoidable shot noise of an ideal photon detector.Noisy Image Original Image
  • 5. Some Important Noise ProbabilityDensity FunctionsGaussian NoiseSalt-and-Pepper (Impulse) NoisePoisson NoiseErlang (Gamma) NoiseExponential NoiseUniform Noise
  • 6. Gaussian NoiseI = imread(eight.tif);J = imnoise(I,gaussian,0.02,0.1);figure, imshow(I)figure, imshow(J)Source Image Image with Gaussian NoiseMATLAB program for adding Gaussian Noise
  • 7. Impulse (Salt and Pepper) Noise
  • 8. MATLAB program for adding Impulse(Salt and Pepper) NoiseI = imread(eight.tif);J = imnoise(I,salt & pepper,0.02);figure, imshow(I)figure, imshow(J)Source Image Image with Salt & Pepper Noise
  • 9. Poisson NoiseA random variable X that obeys a Poissondistribution takes on only nonnegative values;the probability that X = k is where λ is apositive parameter.
  • 10. MATLAB program for adding PoissonNoiseI = imread(eight.tif);J = imnoise(I,poisson);figure, imshow(I)figure, imshow(J)Source Image Image with Salt & Pepper Noise
  • 11. Erlang/Gamma NoiseOriginal Image Image with Gamma Noise
  • 12. Exponential NoiseOriginal Image Image with Gamma Noise
  • 13. Uniform NoiseOriginal Image Image with Gamma Noise
  • 14. ThankYou