noise

I have this image: I want to calculate SNR in it. For this i used code: img=imread('noicy.JPG'); img=double(img(:)); ima=max(img(:)); imi=min(img(:)); ims=std(img(:)); snr=20*log10((ima-imi)./ims) Is that correct code to calculate SNR? The definition of SNR can be found here or here : Both the standard and the industry definition can be used ( 10log(x) and 20log(x) ). check this now, the signal is equal to the mean of the pixel values ( mean(img(:)) ) and the noise is the standard deviation or error value of the pixel values ( std(img(:)) ). You may use either the ratio or the SNR=10*log10

As the question says, I'm looking for a way to plot blurred points using Matplotlib. I don't want to plot a set of points and then apply a filter to blurry the whole image. Instead of it, I would like to plot a set of points, each of them with an associated level of blur. Thank you in advance. Here's another work around. You can display an image at each location instead of a marker using a BboxImage . That way you can blur or manipulate the image any way you want. This tutorial has more about BboxImages . import matplotlib.pyplot as plt from scipy import ndimage from matplotlib.image import

I want to add gaussian noise to a 256x256 greyscale image and then remove it. I tried doing using the following code but all I get is an image which has noise alone. Is it possible to completely remove the noise from the image? And if not to what extent can gaussian noise be reduced? P =0; %mean Q =0.01; %variance R = imnoise(L,'gaussian',P,Q); %L-image subplot(2,1,1); imshow(R); title('Gaussian Noise'); U = conv2(double(R), ones(3)/9, 'same'); U1=uint8(U); subplot(2,1,2); imshow(U1); title('Median Filtered Image'); I'm planning to implement addition and removal of Poisson noise and Salt and

I'm generating terrain in Opengl geometry shader and am having trouble calculating normals for lighting. I'm generating the terrain dynamically each frame with a perlin noise function implemented in the geometry shader. Because of this, I need an efficient way to calculate normals per-vertex based on the noise function (no texture or anything). I am able to take cross product of 2 side to get face normals, but they are generated dynamically with the geometry so I cannot then go back and smooth the face normals for vertex normals. How can I get vertex normals on the fly just using the noise

I need a method to generate 3D simplex noise in python rather quickly. What methods are there out there to solve this problem? $ pip install noise http://pypi.python.org/pypi/noise/ This package is designed to give you simple to use, fast functions for generating Perlin noise in your Python programs. Perlin noise is famously called the "salt" of procedural generation, as it adds considerable flavor in its application. Noise is commonly used for imparting realism in textures, animation and other procedural content generation -- placement of hairs, heights of mountains, density of forests,