Noise models

1. Photoeletronic noise: low-light, hight-light image. Use frame averaging. If available, average N frames of same object If noise is independent frame-to-frame, variance will be reduced by \delta/N
2. Uniform Noise: X_n[n] = X[n] + U[n], where U[n] is (0,1) or (-A, A). Its mean function equals to zero. Use midpoint filter (max+min /2)
   
3. Gaussian Noise: X_n[n] = X[n] + G[n], where G[n]: {mean 0, var = \delta ^ 2}. Use Linear Smoothers to reduce Gaussian noise. Also you can use midpoint filter
   
4. Salt and Pepper Noise:
   X_n[n] = { X[n], (1-p)% of time
   = { 1, (p/2)% of time
   = { 0, (p/2)% of time
   Use median filters to reduce this kind of noise.
5. Mixed Gaussian: X_n[n] = X[n] + MG[n], where
   MG[n] = { (0, \delta ^2 , (1-p)% of time
   = { other, (p)% of time
6. Dust and Scratches: damage associated with photographs. Lines are scratches, and dots are dust. Use median filter or median filter windows to reduce this kind of noise.
7. Structured noise: it is periodic noise. Commonly caused by interference between electronic components. Periodicity means noise power is isolated into a few frequencies. Use band-reject filter. Another kind of structured noise is JPEG noise (aperoidic)