DIP Lecture 1-8 (Digital Image Processing Presentation.pptx

Digital Image Processing ppt

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2. Muhammad Tahir Mumtaz M- Phil in computer science (Computer Vision) University of Central Punjab Lahore, Punjab, Pakistan. M- Phil in computer science (Software Engineering) Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Islamabad, Punjab, Pakistan Ph. D in computer science (Cont..) (Deep Learning) Unisza, Terengganu, Malaysia.

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4. Today’s Topics Signal Analog and Digital Data & Signals Periodic & Aperiodic Signals Digital Signal Processing Digital Image Processing

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7. Analog ANALOG Refers to something that is Continuous CONTINUOUS A set of specific points of data and all possible points between them

8. Digital DIGITAL Refers to something that is Discrete DISCRETE A set of specific points of data with no points in between

9. Analog and Digital Data Analog Data Human Voice Digital Data Data stored in the memory of a computer

10. Analog and Digital Signals

11. Periodic and Aperiodic Signals Periodic Signals (Analog or Digital) Aperiodic

12. Periodic Signals A signal is called Periodic if it completes a pattern within a measurable time frame called a Period and then repeats that pattern over identical subsequent Periods

13. Periodic Signal Example

14. Aperiodic Signals An Aperiodic or Non-Periodic signal is the one that changes constantly without exhibiting (showing) a pattern or cycle that repeats over time

15. Aperiodic Signals

16. Digital Signal Processing Digital signal processing (DSP) refers to various techniques for improving the accuracy and reliability of digital communications. This can involve multiple mathematical operations such as compression, decompression, filtering, equalization, modulation and demodulation to generate a signal of superior quality . i.e Audio, video, image etc.. 16

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18. Analog-to-Digital Converter (ADC) In electronics , an ADC stands for Analog-to-Digital Converter. It is a device that converts continuous-time and continuous-amplitude analog signals (such as from sensors) into discrete-time and discrete-amplitude digital signals for processing by digital systems . 18

19. Digital-to-Analog Converter (DAC) : In the context of audio and technology, a DAC, or Digital-to-Analog Converter, is a device that converts digital audio signals (ones and zeros) into analog signals (continuous electrical voltage) that can be played through headphones or speakers . 19

20. Audio Amp An audio amplifier, often referred to simply as an "amp," is a device used to increase the amplitude (volume) of audio signals. . 20

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22. Applications of DSP Digital image processing Computer graphics Photo manipulation Biomedicine Speech processing Speech recognition Data transmission Audio signal processing Audio data compression  e.g.  MP3 Video data compression Radar Sonar Financial signal processing Economic forecasting Seismology Weather forecasting . 22

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25. Disadvantages Search at least three and write into your notes. Or on this slide… . 25

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27. Image processing Image processing is the process of transforming an image into a digital form and performing certain operations to get some useful information from it. The image processing system usually treats all images as 2D signals when applying certain predetermined signal processing methods. . 27

28. Image An  image  is a visual representation of something. In information technology, the term has several usages: 1) An  image  is a  picture  that has been created or copied and stored in electronic form. An image  can be described in terms of vector graphics or raster graphics. Image – A two-dimensional signal that can be observed by human visual system . 28

29. How are images represented in the computer?

30. Image processing is used for two somewhat different purposes : • improving the visual appearance of images (pictorial information) to a human viewer, and • Preparing (processing) images for measurement of the features and structures present . 30

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32. . 32 Introduction What is Digital Image Processing? Digital Image — a two-dimensional function x and y are spatial coordinates The f is called intensity or gray level at the point (x, y) Digital Image Processing — process digital images by means of computer, it covers low-, mid-, and high-level processes low-level: inputs and outputs are images mid-level: outputs are attributes extracted from input images high-level: an ensemble of recognition of individual objects Pixel — the elements of a digital image

33. Color Image A color image is just three functions pasted together. We can write this as a “vector-valued” function: . 33

34. . 34 Origins of Digital Image Processing Sent by submarine cable between London and New York, the transportation time was reduced to less than three hours from more than a week

35. . 35 Origins of Digital Image Processing

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46. . 46 Sources for Images Electromagnetic (EM) energy spectrum Acoustic Ultrasonic Electronic Artificial images produced by computer

47. 47 Electromagnetic (EM) energy spectrum Major uses Gamma-ray imaging : nuclear medicine and astronomical observations X-rays : medical diagnostics, industry, and astronomy, etc. Ultraviolet : industrial inspection, microscopy, lasers, biological imaging, and astronomical observations Visible and infrared bands : light microscopy, astronomy, remote sensing, industry, and law enforcement Microwave band : radar Radio band : medicine (such as MRI) and astronomy

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49. . 49 Examples: Gama-Ray Imaging

50. . 50 Examples: X-Ray Imaging

51. . 51 Examples: Ultraviolet Imaging

52. . 52 Examples: Light Microscopy Imaging

53. . 53 Examples: Visual and Infrared Imaging

54. . 54 Examples: Infrared Satellite Imaging USA 1993 USA 2003

55. . 55 Examples: Infrared Satellite Imaging

56. . 56 Examples: Automated Visual Inspection

57. . 57 Examples: Automated Visual Inspection The area in which the imaging system detected the plate Results of automated reading of the plate content by the system

58. . 58 Example of Radar Image

59. . 59 Examples: MRI (Radio Band)

60. . 60 Examples: Ultrasound Imaging

61. . 61 Light and EM Spectrum

62. . 62 Light and EM Spectrum The colors that humans perceive in an object are determined by the nature of the light reflected from the object.

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65. Image Acquisition . 65

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86. . 86 A Simple Image Formation Model

87. . 87 Representing Digital Images The representation of an M ×N numerical array as

88. . 88 Representing Digital Images The representation of an M ×N numerical array as

89. . 89 Representing Digital Images The representation of an M ×N numerical array in MATLAB

90. . 90 Representing Digital Images Discrete intensity interval [0, L-1], L=2 k The number b of bits required to store a M × N digitized image b = M × N × k

91. . 91 Representing Digital Images

92. . 92 Spatial and Intensity Resolution Spatial resolution — A measure of the smallest visible detail in an image — stated with line pairs per unit distance, dots (pixels) per unit distance, dots per inch (dpi) Intensity resolution — The smallest discernible change in intensity level — stated with 8 bits, 12 bits, 16 bits, etc.

93. . 93 Spatial and Intensity Resolution

94. . 94 Spatial and Intensity Resolution

95. . 95 Spatial and Intensity Resolution

96. . 96 Image Interpolation Interpolation — Process of using known data to estimate unknown values e.g., zooming, shrinking, rotating, and geometric correction Interpolation (sometimes called resampling ) — an imaging method to increase (or decrease) the number of pixels in a digital image. Some digital cameras use interpolation to produce a larger image than the sensor captured or to create digital zoom http://www.dpreview.com/learn/?/key=interpolation

97. . 97 Examples: Interpolation

98. . 98 Examples: Interpolation

99. . 99 Examples: Interpolation

100. . 100 Examples: Interpolation

101. . 101 Basic Relationships Between Pixels Neighborhood Adjacency Connectivity Paths Regions and boundaries

102. . 102 Basic Relationships Between Pixels Neighbors of a pixel p at coordinates ( x,y ) 4-neighbors of p , denoted by N 4 (p) : (x-1, y), (x+1, y), (x,y-1), and (x, y+1). 4 diagonal neighbors of p , denoted by N D (p) : (x-1, y-1), (x+1, y+1), (x+1,y-1), and (x-1, y+1). 8 neighbors of p , denoted N 8 (p) N 8 (p) = N 4 (p) U N D (p)

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105. . 105 Basic Relationships Between Pixels Adjacency Let V be the set of intensity values 4-adjacency : Two pixels p and q with values from V are 4-adjacent if q is in the set N 4 (p). 8-adjacency : Two pixels p and q with values from V are 8-adjacent if q is in the set N 8 (p).

106. . 106 Basic Relationships Between Pixels Adjacency Let V be the set of intensity values m-adjacency : Two pixels p and q with values from V are m-adjacent if (i) q is in the set N 4 (p), or (ii) q is in the set N D (p) and the set N 4 (p) ∩ N 4 (p) has no pixels whose values are from V.

107. . 107 Basic Relationships Between Pixels Path A (digital) path (or curve) from pixel p with coordinates (x , y ) to pixel q with coordinates ( x n , y n ) is a sequence of distinct pixels with coordinates (x , y ), (x 1 , y 1 ), …, ( x n , y n ) Where (x i , y i ) and (x i-1 , y i-1 ) are adjacent for 1 ≤ i ≤ n. Here n is the length of the path. If (x , y ) = ( x n , y n ), the path is closed path. We can define 4-, 8-, and m-paths based on the type of adjacency used.

108. . 108 Examples: Adjacency and Path 0 1 1 0 1 1 0 1 1 0 2 0 0 2 0 0 2 0 0 0 1 0 0 1 0 0 1 V = {1, 2}

109. . 109 Examples: Adjacency and Path 0 1 1 0 1 1 0 1 1 0 2 0 0 2 0 0 2 0 0 0 1 0 0 1 0 0 1 V = {1, 2} 8-adjacent

110. . 110 Examples: Adjacency and Path 0 1 1 0 1 1 0 1 1 0 2 0 0 2 0 0 2 0 0 0 1 0 0 1 0 0 1 V = {1, 2} 8-adjacent m-adjacent

111. . 111 Examples: Adjacency and Path 1,1 1 1,2 1 1,3 0 1 1 0 1 1 2,1 2 2,2 2,3 0 2 0 0 2 0 3,1 3,2 1 3,3 0 0 1 0 0 1 V = {1, 2} 8-adjacent m-adjacent The 8-path from (1,3) to (3,3): (1,3), (1,2), (2,2), (3,3) (1,3), (2,2), (3,3) The m-path from (1,3) to (3,3): (1,3), (1,2), (2,2), (3,3)

112. . 112 Introduction to Mathematical Operations in DIP Array vs. Matrix Operation Array product Matrix product Array product operator Matrix product operator

113. . 113 Arithmetic Operations Arithmetic operations between images are array operations. The four arithmetic operations are denoted as s(x,y) = f(x,y) + g(x,y) d(x,y) = f(x,y) – g(x,y) p(x,y) = f(x,y) × g(x,y) v(x,y) = f(x,y) ÷ g(x,y)

114. . 114 Example: Addition of Noisy Images for Noise Reduction Noiseless image: f(x,y) Noise: n(x,y) (at every pair of coordinates (x,y), the noise is uncorrelated and has zero average value) Corrupted image: g(x,y) g(x,y) = f(x,y) + n(x,y) Reducing the noise by adding a set of noisy images, {g i (x,y)}

115. . 115 Example: Addition of Noisy Images for Noise Reduction

116. . 116 Example: Addition of Noisy Images for Noise Reduction In astronomy, imaging under very low light levels frequently causes sensor noise to render single images virtually useless for analysis. In astronomical observations, similar sensors for noise reduction by observing the same scene over long periods of time. Image averaging is then used to reduce the noise.

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118. . 118 An Example of Image Subtraction: Mask Mode Radiography Mask h(x,y): an X-ray image of a region of a patient’s body Live images f(x,y): X-ray images captured at TV rates after injection of the contrast medium Enhanced detail g(x,y) g(x,y) = f(x,y) - h(x,y) The procedure gives a movie showing how the contrast medium propagates through the various arteries in the area being observed.

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120. . 120 An Example of Image Multiplication

121. . 121 Set and Logical Operations

122. . 122 Set and Logical Operations Let A be the elements of a gray-scale image The elements of A are triplets of the form (x, y, z), where x and y are spatial coordinates and z denotes the intensity at the point (x, y). The complement of A is denoted A c

123. . 123 Set and Logical Operations The union of two gray-scale images (sets) A and B is defined as the set

124. . 124 Set and Logical Operations

125. . 125 Set and Logical Operations

126. . 126 Spatial Operations Single-pixel operations Alter the values of an image’s pixels based on the intensity. e.g.,

127. . 127 Spatial Operations Neighborhood operations The value of this pixel is determined by a specified operation involving the pixels in the input image with coordinates in S xy

128. . 128 Spatial Operations Neighborhood operations

129. . 129 Geometric Spatial Transformations Geometric transformation (rubber-sheet transformation) — A spatial transformation of coordinates — intensity interpolation that assigns intensity values to the spatially transformed pixels. Affine transform

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131. . 131 Intensity Assignment Forward Mapping It’s possible that two or more pixels can be transformed to the same location in the output image. Inverse Mapping The nearest input pixels to determine the intensity of the output pixel value. Inverse mappings are more efficient to implement than forward mappings.

132. . 132 Example: Image Rotation and Intensity Interpolation

133. . 133 Image Registration Input and output images are available but the transformation function is unknown. Goal: estimate the transformation function and use it to register the two images. One of the principal approaches for image registration is to use tie points (also called control points ) The corresponding points are known precisely in the input and output ( reference ) images.

134. . 134 Image Registration A simple model based on bilinear approximation:

135. . 135 Image Registration

136. . 136 Image Transform A particularly important class of 2-D linear transforms, denoted T(u, v)

137. . 137 Image Transform Given T(u, v), the original image f(x, y) can be recoverd using the inverse tranformation of T(u, v).

138. . 138 Image Transform

139. . 139 Example: Image Denoising by Using DCT Transform

140. . 140 Forward Transform Kernel

141. . 141 The Kernels for 2-D Fourier Transform

142. . 142 2-D Fourier Transform

143. . 143 Probabilistic Methods

144. . 144 Probabilistic Methods

145. . 145 Example: Comparison of Standard Deviation Values

146. . 146 Homework http://cramer.cs.nmt.edu/~ip/assignments.html