This document analyzes the performance of various speckle noise filters using MATLAB. It discusses different types of noise that affect images, such as impulse noise, Gaussian noise, Poisson noise, and speckle noise. It then examines speckle noise filters like the mean, median, Frost, Lee, and Wiener filters, describing how each works to reduce speckle noise in images. The document concludes by comparing the performance of these filters at removing speckle noise while preserving image details and edges.

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.