Borrar filtros
Borrar filtros

AWGN for a grayscale image

4 visualizaciones (últimos 30 días)
Vivek Bhadouria
Vivek Bhadouria el 30 de Ag. de 2011
Hello everyone,
I need to add AWGN to a grayscale image for the purpose of denoising. Till now I was using "imnoise" to add the gaussian noise but somewhere I doubt or I am confused whether I am adding correct noise or not.
Is this the correct method to add AWGN in an image? If not, please tell me how to add this kind of noise.
Thanking you in advance
Vivek Singh Bhadouria
India

Respuesta aceptada

Rick Rosson
Rick Rosson el 31 de Ag. de 2011
Yes, imnoise is the right function to use.
Why are you concerned that it might not be the correct function to use?
Rick
  1 comentario
Vivek Bhadouria
Vivek Bhadouria el 22 de Sept. de 2011
Dear Rick,
There is no problem in using imnoise function. Actually, I am interested in adding "Additive gaussian noise" and "multiplicative gaussian noise" to an image but I am not fully sure about the nature of the gaussian noise being added by imnoise function i.e. its behavior is additive or multiplicative.
Is there any way out to handle this?
Vivek Bhadouria

Iniciar sesión para comentar.

Más respuestas (1)

Wayne King
Wayne King el 22 de Sept. de 2011
Hi, The answer is both.
The Gaussian noise is additive if you use the 'Gaussian' option.
J = imnoise(I,'gaussian',M,V)
The Gaussian noise is multiplicative if you use 'speckle'
J = imnoise(I,'speckle',V)
Hope that helps, Wayne
  4 comentarios
Wayne King
Wayne King el 27 de Sept. de 2011
Sorry Vivek, you're right. I misread the help. The multiplicative option does use rand()
Image Analyst
Image Analyst el 27 de Sept. de 2011
I'm not sure why they call it "speckle." At least it appears to have nothing to do with speckle in the laser imaging sense. My textbook on statistical optics says "Under the assumptions of the given statistical model, the speckle intensity follows a negative exponential law, and speckle phase follows a uniform law." The law (equation) it gives for laser speckle intensity distribution is p(i) = sigma^(-2) * exp(-i / sigma^2). This is definitely different than you'd get from MATLAB which says "adds multiplicative noise to the image I, using the equation J = I+n*I, where n is uniformly distributed random noise with mean 0 and variance v." So MATLAB's speckle noise is not laser speckle noise - it uses some other definition of speckle.

Iniciar sesión para comentar.

Categorías

Más información sobre Image Filtering and Enhancement en Help Center y File Exchange.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by