This document contains 80 questions related to digital signal and image processing. The questions cover topics such as image transforms, filters, noise, compression, segmentation, and more. Justification is required for some questions, while others involve calculations, derivations or explanations of key concepts. The questions vary in difficulty and mark allocation from 5 to 10 marks. They also specify the exam or year in which the question appeared previously.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Image Acquisition and Representation
A Simple Image Formation Model
Image Sampling and Quantization
Image Interpolation
Image quantization
Nearest Neighbor Interpolation
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
요즘 Image관련 Deep learning 관련 논문에서 많이 나오는
용어인 Invariance와 Equivariance의 차이를 알기쉽게 설명하는 자료를 만들어봤습니다. Image의 Transformation에 대해
Equivariant한 feature를 만들기 위하여 제안된 Group equivariant Convolutional. Neural Networks 와 Capsule Nets에 대하여 설명
DISTINGUISH BETWEEN WALSH TRANSFORM AND HAAR TRANSFORMDip transformsNITHIN KALLE PALLY
walsh transform-1D Walsh Transform kernel is given by:
n - 1
g(x, u) = (1/N) ∏ (-1) bi(x) bn-1-i(u)
i = 0
where, N – no. of samples
n – no. of bits needed to represent x as well as u
bk(z) – kth bits in binary representation of z.
Thus, Forward Discrete Walsh Transformation is
N - 1 n - 1
W(u) = (1/N) Σ f(x) ∏ (-1) bi(x) b(u) x = 0 i = 0
Tensor representations in signal processing and machine learning (tutorial ta...Tatsuya Yokota
Tutorial talk in APSIPA-ASC 2020.
Title: Tensor representations in signal processing and machine learning.
Introduction to tensor decomposition (テンソル分解入門)
Basics of tensor decomposition (テンソル分解の基礎)
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
This paper presents a novel technique for
designing an Infinite Impulse Response (IIR) Filter with
Linear Phase Response. The design of IIR filter is always a
challenging task due to the reason that a Linear Phase
Response is not realizable in this kind. The conventional
techniques involve large number of samples and higher
order filter for better approximation resulting in complex
hardware for implementing the same. In addition, an
extensive computational resource for obtaining the inverse
of huge matrices is required. However, we propose a
technique, which uses the frequency domain sampling along
with the linear programming concept to achieve a filter
design, which gives a best approximation for the linear
phase response. The proposed method can give the closest
response with less number of samples (only 10) and is
computationally simple. We have presented the filter design
along with its formulation and solving methodology.
Numerical results are used to substantiate the efficiency of
the proposed method.
Ijcatr03051008Implementation of Matrix based Mapping Method Using Elliptic Cu...Editor IJCATR
Elliptic Curve Cryptography (ECC) gained a lot of attention in industry. The key attraction of ECC over RSA is that it
offers equal security even for smaller bit size, thus reducing the processing complexity. ECC Encryption and Decryption methods can
only perform encrypt and decrypt operations on the curve but not on the message. This paper presents a fast mapping method based on
matrix approach for ECC, which offers high security for the encrypted message. First, the alphabetic message is mapped on to the
points on an elliptic curve. Later encode those points using Elgamal encryption method with the use of a non-singular matrix. And the
encoded message can be decrypted by Elgamal decryption technique and to get back the original message, the matrix obtained from
decoding is multiplied with the inverse of non-singular matrix. The coding is done using Verilog. The design is simulated and
synthesized using FPGA.
요즘 Image관련 Deep learning 관련 논문에서 많이 나오는
용어인 Invariance와 Equivariance의 차이를 알기쉽게 설명하는 자료를 만들어봤습니다. Image의 Transformation에 대해
Equivariant한 feature를 만들기 위하여 제안된 Group equivariant Convolutional. Neural Networks 와 Capsule Nets에 대하여 설명
DISTINGUISH BETWEEN WALSH TRANSFORM AND HAAR TRANSFORMDip transformsNITHIN KALLE PALLY
walsh transform-1D Walsh Transform kernel is given by:
n - 1
g(x, u) = (1/N) ∏ (-1) bi(x) bn-1-i(u)
i = 0
where, N – no. of samples
n – no. of bits needed to represent x as well as u
bk(z) – kth bits in binary representation of z.
Thus, Forward Discrete Walsh Transformation is
N - 1 n - 1
W(u) = (1/N) Σ f(x) ∏ (-1) bi(x) b(u) x = 0 i = 0
Tensor representations in signal processing and machine learning (tutorial ta...Tatsuya Yokota
Tutorial talk in APSIPA-ASC 2020.
Title: Tensor representations in signal processing and machine learning.
Introduction to tensor decomposition (テンソル分解入門)
Basics of tensor decomposition (テンソル分解の基礎)
Adaptive Median Filters
Elements of visual perception
Representing Digital Images
Spatial and Intensity Resolution
cones and rods
Brightness Adaptation
Spatial and Intensity Resolution
A Novel Methodology for Designing Linear Phase IIR FiltersIDES Editor
This paper presents a novel technique for
designing an Infinite Impulse Response (IIR) Filter with
Linear Phase Response. The design of IIR filter is always a
challenging task due to the reason that a Linear Phase
Response is not realizable in this kind. The conventional
techniques involve large number of samples and higher
order filter for better approximation resulting in complex
hardware for implementing the same. In addition, an
extensive computational resource for obtaining the inverse
of huge matrices is required. However, we propose a
technique, which uses the frequency domain sampling along
with the linear programming concept to achieve a filter
design, which gives a best approximation for the linear
phase response. The proposed method can give the closest
response with less number of samples (only 10) and is
computationally simple. We have presented the filter design
along with its formulation and solving methodology.
Numerical results are used to substantiate the efficiency of
the proposed method.
Ijcatr03051008Implementation of Matrix based Mapping Method Using Elliptic Cu...Editor IJCATR
Elliptic Curve Cryptography (ECC) gained a lot of attention in industry. The key attraction of ECC over RSA is that it
offers equal security even for smaller bit size, thus reducing the processing complexity. ECC Encryption and Decryption methods can
only perform encrypt and decrypt operations on the curve but not on the message. This paper presents a fast mapping method based on
matrix approach for ECC, which offers high security for the encrypted message. First, the alphabetic message is mapped on to the
points on an elliptic curve. Later encode those points using Elgamal encryption method with the use of a non-singular matrix. And the
encoded message can be decrypted by Elgamal decryption technique and to get back the original message, the matrix obtained from
decoding is multiplied with the inverse of non-singular matrix. The coding is done using Verilog. The design is simulated and
synthesized using FPGA.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
WAVELET BASED AUTHENTICATION/SECRET TRANSMISSION THROUGH IMAGE RESIZING (WA...sipij
The paper is aimed for a wavelet based steganographic/watermarking technique in frequency domain
termed as WASTIR for secret message/image transmission or image authentication. Number system
conversion of the secret image by changing radix form decimal to quaternary is the pre-processing of the
technique. Cover image scaling through inverse discrete wavelet transformation with false Horizontal and
vertical coefficients are embedded with quaternary digits through hash function and a secret key.
Experimental results are computed and compared with the existing steganographic techniques like WTSIC,
Yuancheng Li’s Method and Region-Based in terms of Mean Square Error (MSE), Peak Signal to Noise
Ratio (PSNR) and Image Fidelity (IF) which show better performances in WASTIR.
IMAGE AUTHENTICATION THROUGH ZTRANSFORM WITH LOW ENERGY AND BANDWIDTH (IAZT)IJNSA Journal
In this paper a Z-transform based image authentication technique termed as IAZT has been proposed to authenticate gray scale images. The technique uses energy efficient and low bandwidth based invisible data embedding with a minimal computational complexity. Near about half of the bandwidth is required compared to the traditional Z–transform while transmitting the multimedia contents such as images with authenticating message through network. This authenticating technique may be used for copyright protection or ownership verification. Experimental results are computed and compared with the existing authentication techniques like Li’s method [11], SCDFT [13], Region-Based method [14] and many more based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Fidelity (IF), Universal Quality Image (UQI) and Structural Similarity Index Measurement (SSIM) which shows better performance in IAZT.
IMAGE AUTHENTICATION THROUGH ZTRANSFORM WITH LOW ENERGY AND BANDWIDTH (IAZT)IJNSA Journal
In this paper a Z-transform based image authentication technique termed as IAZT has been proposed to
authenticate gray scale images. The technique uses energy efficient and low bandwidth based invisible data
embedding with a minimal computational complexity. Near about half of the bandwidth is required
compared to the traditional Z–transform while transmitting the multimedia contents such as images with
authenticating message through network. This authenticating technique may be used for copyright
protection or ownership verification. Experimental results are computed and compared with the existing
authentication techniques like Li’s method [11], SCDFT [13], Region-Based method [14] and many more
based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Fidelity (IF), Universal
Quality Image (UQI) and Structural Similarity Index Measurement (SSIM) which shows better performance
in IAZT.
The Elphinstonian 1988-College Building Centenary Number (2).pdfMukesh Tekwani
This is the 1988 issue of The Elphinstonian, the annual magazine of Elphinstone College, Mumbai. This is the special issue to commemorate the Century of the Elphinstone College Building in Mumbai.
What is gravitation, Newton's law of gravitation, projection of a satellite, derivations, weightlessness explained, change in value of g with altitude, time period of a satellite, binding energy, escape velocity of a satellite,
ISCE-Class 12-Question Bank - Electrostatics - PhysicsMukesh Tekwani
This is a 14 page question bank on the chapters of Electrostatics. This is based on the syllabus of most Board exams such as CBSE, ISCE and state boards.
Extremely important topic for Digital electronics, digital circuits, computer architecture and computer science.
Full video is available on Youtube: https://youtu.be/oyOaXqx06pY
This video explains the method of converting a decimal number to a binary number. Many solved examples are given here and also two exercises which you can attempt on your own and then check the answers.
I have also discussed the concept of LSB (least significant bit) and MSB (most significant bit), and also least significant digit (LSD) and most significant digit (MSD).
This topic is important for following courses: class 11 and 12 computer science of all state boards, class 11 and 12 physics, BSc Computer science, BSc IT, MCA (Masters degree in Computer Applications), BTech, BE (First Year), and many competitive examinations.
Free Lectures on YouTube for IGCSE Physics for the syllabus effective 2020-21. These lectures cover the syllabus of IGCSE and a major part of GCSE syllabus also.
1. The Hidden Meaning of Words in Science Question Papers
2. Scientific Notation or Powers of Ten Notation
3. Units and Base Quantities
4. What is Physics?
What is Cyber Law? Why is cyber security law needed? International cyber law. What is copyright? What are security, controls, privacy, piracy and ethics? Code of ethics for computer professionals. What is cyber insurance?
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
1. mukeshtekwani@outlook.com
Digital Signal and Image Processing
Frequently Asked Questions
In BE (Sem VII) – University of Mumbai
No
Question
JUSTIFY:
1. If the kernel of the image transform is separable and symmetric the transform
can be explained in matrix form. Justify.
2. Laplacian is not a good edge detector – Justify
3. Lossy compression is not suitable for compressing executable files – Justify
4.
5.
6.
7.
Low pass filter is a smoothing filter – Justify
Unit step sequence is a power signal – Justify
If the energy of a signal is finite, its power is zer. Justify
Laplacian is better than gradient for detection of edges – Justify
8. Walsh transform is nothing but sequently ordered Hadamard transform
matrix. Justify
9. All image compression techniques are invertible. Justify
10. For digital image having salt and pepper noise, median filter is best filter.
11. Unit ramp signal is neither energy nor power signal.
12. List and prove any four properties of DFT.
13. Find the circular convolution on the given two sequences x1(n) = {1, -1, 2, -4}
and x2(n) = {1, 2}
14. Compute the Hadamard of the image shown:
2
1
2
1
1
2
3
2
2
3
4
3
1
2
3
2
15. Give the classification of noise in images. Compare restoration and
enhancement. What are the differences between the two? What do they have
in common?
16. Three column vectors are given. Show that they are orthogonal. Also generate
all possible patterns. X1 = [1 1 1], x2 = [-2 1 1], x3 = [0 -1 1]
17. Explain image segmentation using thresholding. How to apply thresholding to
unevenly illuminated images?
18. What is image segmentation? Explain the following methods of image
segmentation. (i) Region growing , (ii) region splitting , (iii) thresholding
19. Determine the Z-transform of the following discrete time signals and also
specify the region of convergence (ROC)
(i) X(n) = {1*, 2, 3, 4},
(ii) X(n) = {1, 3, 5, 7*},
(iii) X(n) = {1, 2, 3, 4*, 5, 6, 7}
20. Explain log transformation. How is gamma correction done?
21. Find the Huffman code for the following stream of data (28 points)
{1,1,1,1,1,1,1, 2,2,2,2,2,2,2, 3,3,3,3,3, 4,4,4,4, 5,5,5, 6,6,7 }
1|Page
Marks
M/YY
5
D10
5
5
5
D10
D10
J12
D10
D10
D11
D11,
J12
D11
5
5
5
10
5
D11
J12
J12
D10
D10
5
D10
10
D10
J12
10
D10
10
D11
10
10
D10
J13
D10
10
10
D10
D10
5
5
5
5
2. mukeshtekwani@outlook.com
22. What do you mean by Gaussian noise and why is averaging filter used to
eliminate it?
23. List down the advantages and disadvantages of Wiener filter.
24. Write short notes on
(i) KL transform (J13)
(ii) JPEG compression
(iii) Hough Transform
(iv) Classification of signals
(v) Discrete Cosine Transform (5) (D10) (J12) (J13)
(vi) Wiener filter (5) (2010)
(vii) Difference between low pass filter and median filter
(viii) Hough transform (5) (D10) (J12)
(ix) Homomorphic filter (5) (D10)
(x) 4,8,m connectivity of image pixels. (5) (D10)
(xi) Sampling and Quantization (5) (J12)
(xii) Wavelet transform (5) (J12)(J13)
(xiii) Properties of Fourier Transform (J13)
5
D10
5
10
each
D10
25.
10
D
2010
26. Obtain linear convolution of two discrete time signals as below:
10
D
2010
27. Find cross-correlation betweeen given signals
X(n) = {1, 2, 0*, 1} and y(n) = {4, 3, 2*, 1}
5
D
2010
28. Find Z transform of x(n) and draw its ROC
10
D
2010
29. Determine the auto corrrelation of the following signal x(n) = {1*, 3, 1, 1}
5
30. Using 4 point FFT algorithm, calculate the 2-D DFT of
10
D
2010
D10
31. Write 8 x 8 Hadamard transform matrix and its signal flow graph. Using the
butetrfly diagram, compute Hadamard transform for x(n) = {1, 2, 3, 4, 1, 2, 1, 2}
32. Perform histogram equalization and draw new equalized histogram of the
following image data
Gray
0
1
2
3
4
5
6
7
Level
10
D10
10
D11
2|Page
3. mukeshtekwani@outlook.com
No of 790 1023 850
656 329 245
122 81
pixels
33. Equalize the given histogram. What happens when we equalize it twice?
Justify.
Grey Level
0
1
2
3
No. of pixels
70
20
7
3
34. Perform histogram equalization for following. Obtain a plot of original as well
as equalized histogram.
Intensity 0
1
2
3
4
5
6
7
No
of 70
100 40
60
0
80
10
40
pixels
35. Whatare the different types of redundancies in digital image? Explain in detail.
36. For the 3-bit 4x4 size image perform following operations.
(i) Thresholding T = 4
(ii) Intensity level slicing with background r1 = 2 and r2 = 5
(iii) bit plane slicing for MSB and LSB planes
(iv) Negation
37.
38.
39.
40.
41.
42.
43.
4
2
3
0
1
3
5
7
5
3
2
1
2
4
6
7
A causal FIR system has three cascaded block, first two of them have individual
impulse responses h1(n) = {1,2,2} h2(n) = u(n) – u(n-2). Find impulse response
of third block h3(n) if an overall impulse response is h(n) = {2, 5, 6, 3, 2, 2}
Explain in detail enhancement techniques used in Spatial domain used for
images.
Explain homomorphic filtering in detail.
Find the DFT of the given image:
0
1
2
1
1
2
3
2
2
3
4
3
1
3
2
3
Define
(i) Eucledean distance
(ii) City block distance
(iii) Chess board distance
(iv) m connectivity
Find the DFT of the given sequence (Use DITFFT algo) : x(n) = {1,2,3,4,4,3,2,1}
Given below is the table of 8 symbols and their frequency of coccurence. Give
the Huffman code for each symbol.
Symbol
S1
S2
S3
S4
S5
S6
S7
S8
Frequency 0.25 0.15 0.06 0.08 0.21 0.14 0.07 0.04
44. Perform the convolution of the following two sequences using Z transforms:
3|Page
10
D10
10
J12
10
D
2010
D
2010
10
10
J12
8
J12
6
6
J12
J12
10
J12
10
10
J12
J12
8
J12
4. mukeshtekwani@outlook.com
45.
46.
47.
48.
X(n ) = (0.2) n and h(n) = (0.3) n u(n)
Find the inverse Z transform H(z) = 1/ [1 – 3z-1 + 0.5 z-2 ] , |z| > 1
Prove that two dimensional fourier transform matrix is an ordinary matrix.
Derive 8 directional Laplacian filter mask
Derive matrix representation of one dimensional Walsh tranbsform for N = 4
from forward Walsh transformation function.
State fidelity objective and and subjective criteria of image evaluation.
Derive the equation of contrast stretching transformation function on the
input image F and obtain the output image R.
6
5
5
5
J12
D12
D12
D12
5
6
D12
D12
8
D12
6
D12
6
D12
54. Segment the following image such that the difference between the maximum
intensity value and minimum intensity value in the segmente region is less
than 18 using split and merge technique.
8
J12
55. Let x(n) be four point sequence with x(k) = {1, 2, 3, 4}. Find the DFT of the
following sequence using X(k).
(i) P(n) = x(n) cos (nπ/2)
(ii) q(n) = 2∆(n) + 3 {Four point u(n) } + 4 x(n)
6
J12
49.
50.
51.
Given
,
(i) Find 3 bit IGS coded image and calculate compression factor and bits per
pixel (BPP).
(ii) Find decoded image and calculate MSK and PSNR.
52. Given h(n) = {1*, 2} find the response of the system to the input x(n) = {1, 2, 3}
using FFT and IFFT.
53.
4|Page
5. mukeshtekwani@outlook.com
56.
8
D12
6
D12
6
D12
6
D12
8
D12
If the gray level intensity changes are to be made as shown in fig below, derive
the necessary expression for obtaining the new pixel value using slope.
(ii) Obtain the new image by applying the above mentioned transformation
function.
(iii) Plot the histogram of input and output image.
(iv) Compare the histogram of input and output image.
57.
Apply the folllowing filter mask W1, W2 and W3 on the input image F and
obtain the output image.
58. Given h(n) = (1/2)n u(n), find the response of the system to the input x(n) =
(1/4)n u(n) using Z transform method.
59. Explain trimmed average filter. Find trimmed average value of the input image
F at the center position for R = 2 and S = 1 wher R is the number of
consecutive pixels to be trimmed from the minimum extreme and S is the
number of consecutive pixels to be trimmed from maximum extreme.
60.
5|Page
6. mukeshtekwani@outlook.com
(ii) Calculate bits per pixel (BPP) and percentage of compression of compressed
image. Donot consider the payload of Huffman table.
61. X(t) = sin(480πt) + 3 sin(720 πt) is sampled with Fs = 600 Hz.
(i) What are the frequencies in radians in the resulting DT signal x(n)?
(ii) If x(n) is passed through an ideal interpolator, what sithe reconstructed
signal?
62. Apply horizontal and vertical line detection mask on the following image F. Use
appropriate threshold value. Assume virtual rows and columns by repeating
border pixel values.
6
D12
6
D12
63. Assume that the edge in the gray level image starts in the first row and ends in
the last row. Find the cost of all possible edges using the following cost
function.
Cost (p, q) = Imax | f(p) – f(q)|
Where Imax is the max intensity value in the image and f(p) and f(q) are pixel
values at points p and q resp. Find the edge with the minimum value of cost.
Plot the graph.
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73.
How to find inverse one dimensional DFT using forward DITFFT flowgraph.
Derive High Boost Filter mask (3 x3)
Bitreversal technique in FFT
Image enhancement using LOG transformation and power law trasformation.
Explain signals and systems with the help of suitable examples. Give
applications of signals and systems.
Find Z transform of the following finite duration signal and state its ROC: X(n) =
{1,2,5,7,0,1}
Given X(n) = {0, 1, 2, 3} find X(k) using DIT-FFT algorithm.
Find convolution of following signals: x(n) = {2, 1, 3, 5} and h(n) = {0, 1, 2, 4}
Determine the sytem function and unit sample response of the system given
by the diffference equation Y(n) = (1/2) Y(n-1) + 2 X(n)
Perform Histogram equalization for the following. Obtain a plot of original as
well as equalized histogram.
Grey
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No of 100
pixels
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74. Given x(n) = {0,1,2,3,4,5,6,7}, find x(k) using DIT-FFT algo.
75. Compute 2D DFT of given image using DIT-FFT algorithm.
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76. Explain in detail enhancement techniques in spatial domain used for images.
77. What is HADAMARD transform? Write a 4x4 Hadamard matrix and its
applications.
78. Explain image restoration and its applications.
79. What do you understand by sampling and quantization with respect to digital
image pocessing? How will you convert an analog image into a digital image?
80. Name and explain different types of redundancies associated with digital
image.
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