Performance analysis of speckle noise filters using MATLAB

1. PERFORMANCE ANALYSIS OF SPECKLE NOISE
FILTERS USING MATLAB
Submitted by,
SANGAVI.G
MOHANA PRIYA.S
III B.Sc., COMPUTER SCIENCE
IMAGE PROCESSING

2. SYNOPSIS:
 INTRODUCTION
 MATLAB
 WHAT IS AN IMAGE?
 DIGITAL IMAGE PROCESSING
 IMAGE ENHANCEMENT
 TYPES OF NOISE IN IMAGE
 SPECKLE NOISE FILTERS
 CONCLUSION

3. INTRODUCTION:
The functionality of every imaging system has
a characteristic disadvantage, affected by
unwanted signals namely noise.
Noise is the undesirable effects produced in the
image, during image acquisition or
transmission.
Filtering is one of the common methods which
are used to reduce the noises.
This paper aims to analyze the performance of
filters like Mean, Median, Wiener, Lee and
Frost.

4. MATLAB:
 It is a multi-paradigm numerical computing
environment and fourth-generation
programming language.
 It is a high-performance language for technical
computing and visualization,
 Typical uses include: Math and computation.
Algorithm development.

5. WHAT IS AN IMAGE?
 An image is an array, or a matrix, of
square pixels (picture elements) arranged
in columns and rows.
 In a (8-bit) grayscale image each picture
element has an assigned intensity that
ranges from 0 to 255.
 A grey scale image is what people
normally call a black and white image, is
used.

6. DIGITAL IMAGE PROCESSING:
 Digital image processing is the use of
computer algorithms to perform image
processing on digital images.
 It allows a much wider range of
algorithms to be applied to the input data
and can avoid problems such as the
build-up of noise and signal distortion
during processing.

7. IMAGE ENHANCEMENT
 Image enhancement is the
process of adjusting digital
images so that the results are
more suitable for display or
further image analysis.
 For example, you can remove
noise, sharpen, or brighten an
image, making it easier to
identify key features.

8. NOISE:
 Noise removal algorithm is the process of
removing or reducing the noise from the
image.
TYPES OF NOISE IN IMAGES:
 Impulse Noise (Salt and Pepper Noise)
 Gaussian Noise (Amplifier Noise)
 Poisson Noise (Photon Noise)
 Speckle Noise

9. SPECKLE NOISE FILTERS:
 Speckle filtering consists of moving a kernel over each
pixel in the image and applying a mathematical
calculation using the pixel values under the kernel and
replacing the central pixel with the calculated value.
 Different speckle noise filters are
 Mean Filter
 Median Filter
 Frost Filter
 Lee filter
 Wiener filter

10. MEAN FILTERS:
 Pomalaza - Raez invented this intuitive
filter and is also called as average
filter.
 The Mean Filter is a linear filter which
uses a mask over each pixel in the
signal.
 The Mean Filter is a simple to average
it into the data but does not remove the
speckles.
 Hence it is used for applications where
resolution and details is not concerned.

11. MEDIAN FILTERS:
This non linear filter invented by Pitas in
1990.
Median filtering is widely used in digital
image processing under certain
conditions, it preserves edges while
removing noise.
The median filter is a robust filter - widely
used as smoothers for various applications.
Hence it removes pulse or speckle noises
effectively.

12. FROST FILTERS:
 It is invented by Frost in 1982.
 The Frost filter replaces the pixel of
interest with a weighted sum of the
values within the next moving kernel.
 The weighting factors decrease with
distance from the pixel of interest.
 The weighting factors increase for the
central pixels as variance within the
kernel increases.

13. LEE FILTERS:
 It is developed by Jong Sen Lee in
1981.
 The Lee filter removes the noise by
minimizing either the mean square
error or the weighted least square
estimation.
 The weighting factors decrease with
distance from the pixel of interest and
increase for the central pixels as
variance within the window increases.

14. WIENER FILTERS:
 It was proposed by Norbert Wiener.
 It is also known as Least Mean
Square Filter.
 Wiener filter works on the basis of
computation of local image
variance.
 Wiener filter results better than
linear filtering.
 Wiener filter requires more
computation time.

15. ANALYSIS OF SPECKLE NOISE FILTERS:
NOISY IMAGE MEAN IMAGE MEDIAN
FILTER
FROST FILTER LEE FILTER WEINER FILTER

16. HOW TO ADD NOISE IN AN IMAGE?

17. CONCLUSION:
 The Mean Filter averages the data and does
not remove the speckles.
 The median filter is a sliding-window spatial
filter and removes pulse or spike noises.
 The computational cost of the median filter is
its very high.
 But the median filter is better than the mean
filter in terms of preserving the edges
between two different features, but it does not
preserve single pixel-wide features, which
will be altered if speckle noise is present.