Digital Image Fundamentals
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This document provides an overview of digital image fundamentals including: - The electromagnetic spectrum and how light is sensed and sampled by sensor arrays to create digital images. - Common sensor technologies like CCD and CMOS sensors and how they work. - How digital images are represented through spatial and intensity discretization via sampling and quantization. - Factors that affect image quality like spatial and intensity resolution. - Concepts like aliasing, moire patterns, and their relationship to sampling rates. - Basic image processing techniques like zooming, shrinking, and relationships between pixels.
Digital Image Fundamentals
- 1. Digital Image Fundamentals Kalyan Acharjya kalyan5.blogspot.in Lecture 6-7 Unit 2 1
- 2. Unit 2 • Image Perception in Eye • Light and Electromagnetic Spectrum • Image Sensing and Acquisition using Sensor array. • Image Sampling and Quantization • Representation of Digital Image • Spatial and Grey Level Resolutions • Alising and Moire Pattern • Zooming and Shrinking Digital Image • Relationship Between Pixels 2
- 3. Disclaimer This PPT is prepared for Academic use only. Most of the Images and Contents are copied from Image Processing Book (Gonzalez) and Internet. 3
- 4. Image Sensing and Acquisition using Sensor array 4
- 5. Light and the Electromagnetic Spectrum • Relationship between frequency ( ) and wavelength ( ) , where c is the speed of light • Energy of a photon where h is Planck’s constant c hE
- 6. Light and the Electromagnetic Spectrum • Three basic quantities described the quality of a chromatic light source: – Radiance: the total amount energy that flow from the light source (can be measured) – Luminance: the amount of energy an observer perceives from a light source (can be measured) – Brightness: A subjective descriptor of light perception; perceived quantity of light emitted (cannot be measured) 6
- 7. CCD and CMOS Sensors? • Array of Diodes (photo-diodes) that produce a voltage: – Linearly Proportional to the AMOUNT incident light. – Non-linearly Dependant to the WAVELENGTH • Built out of layers of Silicon – Silicone is sensitive to light – Layers add functionality–different layers perform different functions. (called ‘die’) 7
- 8. CCD vs. CMOS • Create high-quality, low- noise images. • Greater sensitivity and fidelity • 100 times more power Require • Specialized assembly lines • Older and more developed technology • More susceptible to noise • Light sensitivity is lower • Consume little power • Easy to Manufacture • Cheaper Picture quality, sensitivity and cost vs. Cost and battery life. 8
- 9. Types of CCD and CMOS CCD and CMOS Rotational Lens • Cheaper • Good quality • 3 frames req’d – only stationary objects Filter Array • Cheap • Easy • Small Beam Splitters • Expensive • High Quality • One frame required Sony’s FS700, F5 and F55 cameras all have 4K sensors. 9
- 10. Sampling–Analog Signal • Periodic sampling, the process of representing a continuous signal with a sequence of discrete data values, uses in the field of digital signal processing. • In practice, sampling is performed by applying a continuous signal to an analog-to-digital (A/D) converter whose output is a series of digital values. • With regard to sampling, the primary concern is how fast must the given continuous signal be sampled in order to preserve its information content. 10
- 11. Representation of a Digital Image • An Image has infinite intensity value. • Also infinite picture point . • Digitization of image. Spatial discretization by Sampling. Intensity discretization by Quantization. 11
- 12. Representation of a Digital Image 12
- 13. Digital Images • Binary Image: Each Pixel is just Black and white, i.e. 0 or 1 <462x493 logical> • Gray Scale Image: Each Pixel is shade of Gray, its 0(black) to white(255),i.e. each pixel~8 bits <462x493 unit8> • Color Image or RGB Image, Each pixel corresponds to 3 values. <462x493x3 unit8> 13
- 14. Spatial Resolution • Gray level L=2k • L is discrete level allowed to each pixel. • M and N are spatial • Halve and Double • The number of bits required to store digital image b=MxNxk • When M=N, b= kN2 14
- 16. Spatial Resolution by Re-sampling 16
- 18. Bit Planes of Grey Scale image • Grey scale images can be transformed into a sequence of binary images by breaking them into bit planes. 18
- 19. Sampling Rate and Aliasing • Nyquist rate fs ≥2fm, fs: Sampling frequency fm: Max Message Signal Frequency • If the original waveform does not vary much over the duration of t, then we will also obtain a good construction. Oversampling, i.e., using a sampling rate that is much greater than the Nyquist rate, can ensure this. • fs = fm • Under Sampling fs<fm result of aliasing. 19
- 21. Aliasing and Moiré Pattern • All signals (functions) can be shown to be made up of a linear combination sinusoidal signals (sines and cosines) of different frequencies. (Chapter 4) • For physical reasons, there is a highest frequency component in all real world signals. • Theoretically, – if a signal is sampled at more than twice its highest frequency component, then it can be reconstructed exactly from its samples. – But, if it is sampled at less than that frequency (called under sampling), then aliasing will result. – This causes frequencies to appear in the sampled signal that were not in the original signal. – The Moiré pattern shown the vertical low frequency pattern is a new frequency not in the original patterns. 21
- 22. Moiré Pattern 22
- 23. ALIASING AND MOIRÉ Pattern • Aliasing is the visual stair-stepping of edges that occurs in an image when the resolution is too low. • Anti-aliasing is the smoothing of jagged edges in digital images by averaging the colors of the pixels at a boundary. • The letter on the left is aliased. The letter on the right has had anti-aliasing applied to make the edges appear smoother. 23
- 24. Zooming and Shrinking Digital Images • Zooming: Increasing the number of pixels in an image so that the image appears larger – Nearest Neighbor Interpolation • For example: pixel replication--to repeat rows and columns of an image – Bilinear interpolation • Smoother – Higher order interpolation • Image shrinking: Sub-sampling 24
- 25. Zooming and Shrinking Digital Images Nearest Neighbor Interpolation (Pixel replication) Bilinear interpolation 25
- 26. Some Basic Relationships Between Pixels Neighbors of a Pixel There are three kinds of neighbors of a pixel- • N4(p) 4-neighbors: the set of horizontal and vertical neighbors • ND(p) diagonal neighbors: the set of 4 diagonal neighbors • N8(p) 8-neighbors: union of 4-neighbors and diagonal neighbors O O O O X O O O O O O X O O O O X O O
- 27. Some Basic Relationships Between Pixels Adjacency: – Two pixels that are neighbors and have the same grey-level (or some other specified similarity criterion) are adjacent – Pixels can be 4-adjacent, diagonally adjacent, 8-adjacent, or m-adjacent. m-adjacency (mixed adjacency): – Two pixels p and q of the same value (or specified similarity) are m-adjacent if either • (i) q and p are 4-adjacent, or • (ii) p and q are diagonally adjacent and do not have any common 4- adjacent neighbors. • They cannot be both (i) and (ii).
- 28. Some Basic Relationships Between Pixels • An example of adjacency: 28
- 29. Some Basic Relationships Between Pixels • Distance Measures – Distance function: a function of two points, p and q, in space that satisfies three criteria – The Euclidean distance De(p, q) ),,(),()( 0),()( pqDqpDb qpDa 22 )()(),( tysxqpDe
- 31. Internal Marks Distribution 31 Maximum Marks Your Score (MM:30) Mid Term I 20 x1 Mid Term I 20 x2 Test 10 x3 Attendance 10 x4 Total=60 Internal Score: (x1+x2+x3+x4)/2 (Minimum)
- 32. Thank You for Your Attention This PPT is available at kalyan5.blogspot.in For Feedback Kindly visit https://goo.gl/iPviBm Contact E-mail: kalyan.acharjya@gmail.com Good Luck For Your Mid Term I Examination 32