Question

I want to estimate the noise in an image.

Let s assume the model of an Image + White Noise. Now I want to estimate the Noise Variance.

My method is to calculate the Local Variance (3*3 up to 21*21 Blocks) of the image and then find areas where the Local Variance is fairly constant (By calculating the Local Variance of the Local Variance Matrix). I assume those areas are "Flat" hence the Variance is almost "Pure" noise.

Yet I don t get constant results.

Is there a better way?

Thanks.

P.S. I can t assume anything about the Image but the independent noise (Which isn t true for real image yet let s assume it).

Answer 1

You can use the following method to estimate the noise variance (this implementation works for grayscale images only):

def estimate_noise(I):

  H, W = I.shape

  M = [[1, -2, 1],
       [-2, 4, -2],
       [1, -2, 1]]

  sigma = np.sum(np.sum(np.absolute(convolve2d(I, M))))
  sigma = sigma * math.sqrt(0.5 * math.pi) / (6 * (W-2) * (H-2))

  return sigma

Reference: J. Immerkær, “Fast Noise Variance Estimation”, Computer Vision and Image Understanding, Vol. 64, No. 2, pp. 300-302, Sep. 1996 [PDF]

Answer 2

Scikit Image has an estimate sigma function that works pretty well:

http://scikit-image.org/docs/dev/api/skimage.restoration.html#skimage.restoration.estimate_sigma

it also works with color images, you just need to set multichannel=True and average_sigmas=True:

import cv2
from skimage.restoration import estimate_sigma

def estimate_noise(image_path):
    img = cv2.imread(image_path)
    return estimate_sigma(img, multichannel=True, average_sigmas=True)

High numbers mean low noise.

Answer 3

The problem of characterizing signal from noise is not easy. From your question, a first try would be to characterize second order statistics: natural images are known to have pixel to pixel correlations that are -by definition- not present in white noise.

In Fourier space the correlation corresponds to the energy spectrum. It is known that for natural images, it decreases as 1/f^2 . To quantify noise, I would therefore recommend to compute the correlation coefficient of the spectrum of your image with both hypothesis (flat and 1/f^2), so that you extract the coefficient.

Some functions to start you up:

import numpy
def get_grids(N_X, N_Y):
    from numpy import mgrid
    return mgrid[-1:1:1j*N_X, -1:1:1j*N_Y]

def frequency_radius(fx, fy):
    R2 = fx**2 + fy**2
    (N_X, N_Y) = fx.shape
    R2[N_X/2, N_Y/2]= numpy.inf

    return numpy.sqrt(R2)

def enveloppe_color(fx, fy, alpha=1.0):
    # 0.0, 0.5, 1.0, 2.0 are resp. white, pink, red, brown noise
    # (see http://en.wikipedia.org/wiki/1/f_noise )
    # enveloppe
    return 1. / frequency_radius(fx, fy)**alpha #

import scipy
image = scipy.lena()
N_X, N_Y = image.shape
fx, fy = get_grids(N_X, N_Y)
pink_spectrum = enveloppe_color(fx, fy)

from scipy.fftpack import fft2
power_spectrum = numpy.abs(fft2(image))**2

I recommend this wonderful paper for more details.

友情链接