This document summarizes a student's analysis of different image filtering techniques to reduce noise. It outlines the objective to compare mean, median, adaptive median, and bilateral filters. It introduces various types of image noise like salt and pepper, Gaussian, and speckle noise. Performance is analyzed using PSNR scores. Adaptive median filtering achieved the best results for salt and pepper noise below 0.5 density and Gaussian noise. Average filtering worked best for speckle noise, but frequency domain filters are needed to significantly reduce speckle noise. PSNR is limited and SSIM would provide a better quality assessment.

1. Analysis of Different Type of Filtering
Techniques for Reducing Various Noises
Under the Supervision of
Dr. Shawli Bardhan
Department of Computation
INDIAN INSTITUTE OF INFORMATION
TECHNOLOGY UNA, HIMACHAL PRADESH
Presented by
Tapendra Kumar
Department of Computing
3rd semester
19329
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2. OUTLINE
□ Objective
□ Introduction
□ Noises
□ Image Filters
□ Performance Analysis
□ Work Plan
□ Result & Simulation
□ Conclusion
11/22/2
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3. INTRODUCTION
 In the real-world signals do not exist without noise, which arises during image
acquisition (digitization) and/or transmission.
 When images are acquired using a camera, light level and sensor temperature are major
factors affecting the amount of noise. During transmission, images are corrupted mainly
due to interference in the channel use for transmission.
 Removing noise from images is an important problem in image processing.
 Image de-noising is an vital image processing task i.e. as a process itself as well a
component in other processes. The important property of a good image de-noising
model is that should completely remove noise as far as possible as well as preserve
edges.
 Traditionally there are two types of models i.e. linear model and non-liner model.
 Liner filter’s mathematical simplicity and the existence of some desirable properties
made them easy to design and implement. The benefits of linear noise removing models
is the speed and the limitations of the linear models is, the models are not able to
preserve edges of the images in an efficient manner.
 Non-linear models can handle edges in a much better way than linear models.
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4. Noise in Digital Image
□Noise refers to any external and unwanted information that interferes with a
transmission signal.
□Noise can diminish transmission strength and disturb overall communication efficiency.
In communications, noise can be created by radio waves, power lines, lightning and bad
connections
□Image noise is the random variation of brightness or color information in images
produced by the sensor and circuitry of a scanner or digital camera.
□Image noise can also originate in film grain and in the unavoidable shot noise of an
ideal photon detector. Image noise is generally regarded as an undesirable by-product of
image capture
□There are several different type of noises. But I will mainly focus on three type-
1. AMPLIFIER NOISE (GAUSSIAN NOISE)
2. SALT-AND-PEPPER NOISE
3. SPECKLE NOISE

5. Salt & Pepper Noise
□ An image containing salt-and-pepper noise will have dark pixels
in bright regions and bright pixels in dark regions
□ This type of noise can be caused by dead pixels, analog-to-
digital converter errors, bit errors in transmission, etc.
□ . It is also known as impulse noise. This noise can be caused by
sharp and sudden disturbances in the image signal.
Fig : Image With Salt and Pepper Noise 5

6. Gaussian Noise
Year Author Disease
Description
Purpose ROI
Extraction
Method
Statistical Features
6
 Gaussian noise is statistical noise having a probability density function (PDF) equal
to that of the normal distribution, which is also known as the Gaussian
distribution.
 𝐺 𝑧 =
1
2𝜋𝜎
𝑒
−(𝑧−𝜇)2
𝜎2
 Principal sources of Gaussian noise in digital images arise during acquisition e.g.
sensor noise caused by poor illumination and/or high temperature, and/or
transmission, or thermal vibration of atoms and discrete nature of radiation of
warm objects.
Fig : Image with Gaussian Noise

7. Speckle Noise
Year Author Disease
Description
Purpose ROI
Extraction
Method
Statistical Features
6
 Speckle is a granular interference that inherently exists in and degrades the quality
of the active synthetic aperture radar (SAR), medical ultrasound and optical
coherence tomography images
 Speckle is due to the diffused scattering, which occurs when an ultrasound pulse
arbitrarily interferes with the tiny particles or objects on a scale comparable to the
sound wavelength. The backscattered echoes from irresolvable random tissue in
uniformities in ultrasound imaging and sar imaging from objects in undergo
constructive and destructive interferences resulting in mottled b-scan image.
Speckle noise is typically modelled as multiplicative noise, therefore resultant
signal is the product of speckle signal and original noise.
I (i, j) = S (i, j) * N (i, j)

8. Image Filtering
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 Filtering is a technique used for modifying or enhancing an image like highlight
certain features or remove other features. Image filtering include smoothing,
sharpening, and edge enhancement. It may be applied in either
 spatial domain (Filter is a mathematical operation of a grid of numbers – Smoothing,
sharpening, measuring texture)
 Or Frequency domain(Filtering is a way to modify the frequencies of images –
Denoising, sampling, image compression)
 In this project we are performing comparative analysis of Mean Filter, Median
Filter, Adaptive Median Filter, Bilateral Filter for reduction of noises we have
discussed before.

9. Mean Filter
□ Mean filtering is a simple, intuitive and easy to implement method to reduce
noise in images by reducing the amount of intensity variation between one
pixel and the next.
□ The idea of mean filtering is simply to replace each pixel value in an image
with the mean or average value of its neighbours, including itself. This has
the effect of eliminating pixels values which are unrepresentative of their
surrounding.
□ The main problem with mean filtering is that a single pixel with a very
unrepresentative value can significantly affect the mean value of all the
pixels in its neighbourhood.

10. Median Filter
 The median filter is a non-liner digital filter technique
 A median filter operates over a window by selecting the median intensity in the
window.
 One of the major problems with the median filter is that it is relatively expensive and
complex to compute. To find the median it is necessary to sort all the values in the
neighborhood into numerical order and this is relatively slow, even with fast sorting
algorithms such as quick sort.

11. Adaptive Median Filter
 The Adaptive Median Filter performs spatial processing to determine which pixels in
an image have been affected by impulse noise. The Adaptive Median Filter classifies
pixels as noise by comparing each pixel in the image to its surrounding neighbor pixels

12. Bilateral Filter
 The Adaptive Median Filter performs spatial processing to determine which pixels in
an image have been affected by impulse noise. The Adaptive Median Filter classifies
pixels as noise by comparing each pixel in the image to its surrounding neighbor pixels

13. Performance Analysis
Comparing restoration results requires a measure of image quality. The mean-squared
error (MSE) between two images fij and gij is:
𝐌𝐒𝐄 =
𝟏
𝒎𝒏
𝒊=𝟎
𝒎−𝟏
𝒋=𝟎
𝒏−𝟏
𝒇𝒊𝒋 − 𝒈𝒊𝒋
𝟐
One problem with mean-squared error is that it depends strongly on the image intensity
scaling. Peak Signal-to-Noise Ratio (PSNR) avoids this problem by scaling the MSE
according to the image range:
𝑷𝑺𝑵𝑹 = 𝟏𝟎 𝒍𝒐𝒈 𝟏𝟎
𝒎𝒂𝒙𝒊
𝟐
𝑴𝑺𝑬
= 𝟐𝟎. 𝒍𝒐𝒈 𝟏𝟎 𝒎𝒂𝒙𝒊 − 𝟏𝟎 𝒍𝒐𝒈 𝟏𝟎 𝑴𝑺𝑬
For colour images with three RGB values per pixel, the definition of PSNR is the same
except the MSE is the sum over all squared value differences (now for each colour, i.e.
three times as many differences as in a monochrome image) divided by image size and by
three. Alternately, for colour images the image is converted to a different colour space and
PSNR is reported against each channel of that colour space, e.g., YCbCr or HSL. The lower
the value of MSE, the lower the error and the higher value of PSNR is the higher of image
quality.

14. WORK PLAN…..
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15. Work Flow
Image
f(x,y)
Median
Filter
Bilateral
Filter
Calculate
Psnr
Mean
Filter
AMF
Add Noise
n(xy)

16. Results and Simulations
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1. Salt & Pepper Noise
Salt&Peppeer Desnsity 0.1 0.2 0.3 0.4 0.5
PSNR for Average Filter 29.4483 28.6034 28.2380 28.0465 27.9398
PSNR for Median Filter 30.0296 29.0501 28.5788 28.3049 28.1127
PSNR for Adaptive Median Filter 35.5714 35.3973 35.1115 34.7565 34.2827
PSNR for Bilateral Filter 30.6232 29.2912 28.6618 28.3607 28.1616

17. 12
2. Gaussian Noise
Gaussian
Noise with
mean = 0
and S.D.
25 50 100 150 150
PSNR for
Average
Filter
28.6708 27.7106 27.4646 27.4972 27.5286
PSNR for
Median
Filter
29.4132 28.1268 27.6755 27.6122 27.6144
PSNR for
Adaptive
Median
Filter
29.3387 28.3360 27.9415 27.8511 27.8316
PSNR for
Bilateral
Filter
28.7774 27.7188 27.6777 27.7475 27.8008

18. 12
3. Speckle Noise
Speckle Noise with mean = 0 and
Variance
0.05 0.1 0.15 0.20 0.25
PSNR for Average Filter 32.314
5
31.283
6
30.720
2
30.3746 30.094
PSNR for Median Filter 31.709
3
30.709
5
30.212
1
29.8974 29.644
7
PSNR for Adaptive Median Filter 30.532
8
29.686
0
29.283
7
29.0467 28.873
3
PSNR for Bilateral Filter 31.735
1
30.517
5
29.978
0
229.626
2
29.394
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19. CONCLUSION AND FUTURE WORK
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A comparative analysis of adaptive average, median, adaptive median and bilateral
filters was performed based on the obtained values of PSNR. Refereeing to obtained
experimental results I can conclude the following:
 If the 3 channels of the RGB colour image were affected by salt&pepper noise with
density less than 0.5, it is preferable to use adaptive median filter. Noised image
filtered by amf has almost same quality and colour distribution compared with
original image
 If 3 channels of the RGB colour image are affected with Gaussian noise it is
preferable to use adaptive median filter but when S.D. of gaussian noise is low then
it is good to use median filter.
 If three channels of the RGB colour image is affected with speckle noise then it is
preferable to use average filter among these four filters. But these filters can’t
reduce significant noise and even after filtration denoised image doesn’t contain
much details. So, we have to apply frequency domain filters for reducing speckle
noise.
 We found that the main limitation of using PSNR for quality assessment of images
is that it relies strictly on numeric comparison and does not actually take into
account any level of biological factors of the human vision system such as
the structural similarity index (SSIM).