Noise in imaging technology
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Noise in Imaging Technology. Two plagues in image acquisition Noise interference Blur (motion, out-of-focus, hazy weather) Difficult to obtain high-quality images as imaging goes Beyond visible spectrum Micro-scale (microscopic imaging) Macro-scale (astronomical imaging). What is Noise?.

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Noise in Imaging Technology

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Noise in imaging technology

Noise in Imaging Technology

  • Two plagues in image acquisition

    • Noise interference

    • Blur (motion, out-of-focus, hazy weather)

  • Difficult to obtain high-quality images as imaging goes

    • Beyond visible spectrum

    • Micro-scale (microscopic imaging)

    • Macro-scale (astronomical imaging)


What is noise

What is Noise?

  • noise means any unwanted signal

  • One person’s signal is another one’s noise

  • Noise is not always random and randomness is an artificial term

  • Noise is not always bad.


Stochastic resonance

Stochastic Resonance

no noise

heavy noise

light noise


Noise in imaging technology

Image Denoising

  • Where does noise come from?

    • Sensor (e.g., thermal or electrical interference)

    • Environmental conditions (rain, snow etc.)

  • Why do we want to denoise?

    • Visually unpleasant

    • Bad for compression

    • Bad for analysis


Noisy image examples

Noisy Image Examples

thermal imaging

electrical interference

physical interference

ultrasound imaging


Noise removal techniques

Noise Removal Techniques

Linear filtering

Nonlinear filtering

Recall

Linear system


Noise in imaging technology

Image Denoising

  • Introduction

  • Impulse noise removal

    • Median filtering

  • Additive white Gaussian noise removal

    • 2D convolution and DFT

  • Periodic noise removal

    • Band-rejection and Notch filter


Impulse noise salt pepper noise

Impulse Noise (salt-pepper Noise)

Definition

Each pixel in an image has the probability of p/2 (0<p<1) being

contaminated by either a white dot (salt) or a black dot (pepper)

with probability of p/2

noisy pixels

with probability of p/2

clean pixels

with probability of 1-p

X: noise-free image, Y: noisy image

Note: in some applications, noisy pixels are not simply black or white,

which makes the impulse noise removal problem more difficult


Numerical example

Numerical Example

P=0.1

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 255 0 128 128 128 128 128 128

128 128 128 128 0 128 128 128 128 0

128 128 128 128 128 128 128 128 128 128

128 128 0 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128 128 128

0 128 128 128 128 255 128 128 128 128

128 128 128 128 128 128 128 128 128 255

128 128 128 128 128 128 128 255 128 128

X

Y

Noise level p=0.1 means that approximately 10% of pixels are contaminated by

salt or pepper noise (highlighted by red color)


Matlab command

MATLAB Command

>Y = IMNOISE(X,'salt & pepper',p)

Notes:

 The intensity of input images is assumed to be normalized to [0,1].

If X is double, you need to do normalization first, i.e., X=X/255;

 The default value of p is 0.05 (i.e., 5 percent of pixels are contaminated)

 imnoise function can produce other types of noise as well (you need to

change the noise type ‘salt & pepper’)


Impulse noise removal problem

Impulse Noise Removal Problem

filtering

algorithm

denoised

image

^

X

^

Can we make the denoised image X as close

to the noise-free image X as possible?

Noisy image Y


Median operator

Median Operator

  • Given a sequence of numbers {y1,…,yN}

    • Mean: average of N numbers

    • Min: minimum of N numbers

    • Max: maximum of N numbers

    • Median: half-way of N numbers

Example

sorted


1d median filtering

1D Median Filtering

y(n)

W=2T+1

MATLAB command: x=median(y(n-T:n+T));

Note: median operator is nonlinear


Numerical example1

Numerical Example

T=1:

Boundary

Padding


2d median filtering

2D Median Filtering

x(m,n)

W:

(2T+1)-by-(2T+1) window

MATLAB command: x=medfilt2(y,[2*T+1,2*T+1]);


Numerical example2

Numerical Example

225 225 225 226 226 226 226 226

225 225 255 226 226 226 225 226

226 226 225 226 0 226 226 255

255 226 225 0 226 226 226 226

225 255 0 225 226 226 226 255

255 225 224 226 226 0 225 226

226 225 225 226 255 226 226 228

226 226 225 226 226 226 226 226

0 225 225 226 226 226 226 226

225 225 226 226 226 226 226 226

225 226 226 226 226 226 226 226

226 226 225 225 226 226 226 226

225 225 225 225 226 226 226 226

225 225 225 226 226 226 226 226

225 225 225 226 226 226 226 226

226 226 226 226 226 226 226 226

^

X

Y

Sorted: [0, 0, 0, 225, 225, 225, 226, 226, 226]


Image example

Image Example

P=0.1

denoised

image

^

Noisy image Y

X

3-by-3 window


Idea of improving median filtering

Idea of Improving Median Filtering

  • Can we get rid of impulse noise without affecting clean pixels?

    • Yes, if we know where the clean pixels are

      or equivalently where the noisy pixels are

  • How to detect noisy pixels?

    • They are black or white dots


Median filtering with noise detection

Median Filtering with Noise Detection

Noisy image Y

Median filtering

x=medfilt2(y,[2*T+1,2*T+1]);

Noise detection

C=(y==0)|(y==255);

Obtain filtering results

xx=c.*x+(1-c).*y;


Image example1

Image Example

noisy

(p=0.2)

clean

with

noise

detection

w/o

noise

detection


Noise in imaging technology

Image Denoising

  • Introduction

  • Impulse noise removal

    • Median filtering

  • Additive white Gaussian noise removal

    • 2D convolution and DFT

  • Periodic noise removal

    • Band-rejection and Notch filter


Additive white gaussian noise

Additive White Gaussian Noise

Definition

Each pixel in an image is disturbed by a Gaussian random variable

With zero mean and variance 2

X: noise-free image, Y: noisy image

Note: unlike impulse noise situation, every pixel in the image contaminated

by AWGN is noisy


Numerical example3

Numerical Example

128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128

128 128 128 128 128 128 128 128

128 128 129 127 129 126 126 128

126 128 128 129 129 128 128 127

128 128 128 129 129 127 127 128

128 129 127 126 129 129 129 128

127 127 128 127 129 127 129 128

129 130 127 129 127 129 130 128

129 128 129 128 128 128 129 129

128 128 130 129 128 127 127 126

2=1

X

Y


Matlab command1

MATLAB Command

>Y = IMNOISE(X,’gaussian',m,v)

or

>Y = X+m+randn(size(X))*v;

rand() generates random numbers uniformly distributed over [0,1]

Note:

randn() generates random numbers observing Gaussian distribution

N(0,1)


Image denoising

Image Denoising

filtering

algorithm

denoised

image

^

X

Question: Why not use median filtering?

Hint: the noise type has changed.

Noisy image Y


1d linear filtering

1D Linear Filtering

g(n)

h(n)

f(n)

See review section

Linear convolution

- Linearity

- Time-invariant property


Fourier series

Fourier Series

forward

inverse

time-domain convolution

frequency-domain multiplication

Note that the input signal is a discrete sequence

while its FT is a continuous function


Filter examples

Filter Examples

|H(w)|

Low-pass (LP)

h(n)=[1,1]

HP

LP

|h(w)|=2cos(w/2)

High-pass (LP)

h(n)=[1,-1]

w

|h(w)|=2sin(w/2)


2d linear filtering

2D Linear Filtering

g(m,n)

h(m,n)

f(m,n)

2D convolution

MATLAB function: C = CONV2(A, B)


2d filtering two sequential 1d filtering

2D Filtering=Two Sequential 1D Filtering

Just as we have observed with 2D transform, 2D (separable) filtering can be viewed as two sequential 1D filtering operations: one along row direction and the other along column direction

The order of filtering does not matter

h1 : 1D filter


Fourier series 2d case

Fourier Series (2D case)

spatial-domain convolution

frequency-domain multiplication

Note that the input signal is discrete

while its FT is a continuous function


Filter examples1

Filter Examples

|h(w1,w2)|

Low-pass (LP)

h1(n)=[1,1]

1D

|h1(w)|=2cos(w/2)

h(n)=[1,1;1,1]

2D

w2

|h(w1,w2)|=4cos(w1/2)cos(w2/2)

w1


Image dft example

Image DFT Example

choice 1: Y=fft2(X)

Original ray image X


Image dft example con t

Image DFT Example (Con’t)

choice 1: Y=fft2(X)

choice 2: Y=fftshift(fft2(X))

Low-frequency at four corners

Low-frequency at the center

FFTSHIFT Shift zero-frequency component to center of spectrum.


Gaussian filter

Gaussian Filter

FT

MATLAB code:

>h=fspecial(‘gaussian’, HSIZE,SIGMA);


Noise in imaging technology

Image Example

denoised

noisy

denoised

PSNR=20.2dB

PSNR=24.4dB

PSNR=22.8dB

(=1)

(=1.5)

(=25)

Matlab functions: imfilter, filter2


Gaussian filter heat diffusion

Gaussian Filter=Heat Diffusion

Linear Heat Flow Equation:

Isotropic diffusion:

A Gaussian filter

with zero mean

and variance of t

scale


Basic idea of nonlinear diffusion

Basic Idea of Nonlinear Diffusion*

y

I(x,y)

x

image I

Diffusion should be anisotropic

instead of isotropic

image I viewed as a 3D surface (x,y,I(x,y))


Noise in imaging technology

Image Denoising

  • Introduction

  • Impulse noise removal

    • Median filtering

  • Additive white Gaussian noise removal

    • 2D convolution and DFT

  • Periodic noise removal

    • Band-rejection and Notch filter


Periodic noise

Periodic Noise

  • Source: electrical or electromechanical interference during image acquistion

  • Characteristics

    • Spatially dependent

    • Periodic – easy to observe in frequency domain

  • Processing method

    • Suppressing noise component in frequency domain


Image example2

Image Example

spatial

Frequency (note the four pairs of bright dots)


Band rejection filter

Band Rejection Filter

w2

w1


Image example3

Image Example

After filtering

Before filtering


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