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D igital Image Processing: Digital Imaging Fundamentals

D igital Image Processing: Digital Imaging Fundamentals. Chapter 2 2012 Teacher: Remah W. Al- Khatib. Contents. This lecture will cover: The human visual system Light and the electromagnetic spectrum Image representation Image sensing and acquisition

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D igital Image Processing: Digital Imaging Fundamentals

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  1. Digital Image Processing:Digital Imaging Fundamentals Chapter 2 2012 Teacher: Remah W. Al-Khatib

  2. Contents This lecture will cover: • The human visual system • Light and the electromagnetic spectrum • Image representation • Image sensing and acquisition • Sampling, quantisation and resolution

  3. Human Visual System • The best vision model we have! • It is one of the most sophisticated image processing and analysis systems. • Knowledge of how images form in the eye can help us with processing digital images • Its understanding would also help in the design of efficient, accurate and effective computer/machine vision systems.

  4. Illustration of Human Eye

  5. Illustration of Human Eye

  6. Image Formation in eye andcamera

  7. Image formation in the Eye

  8. Sampling, Quantisation AndResolution In the following slides we will consider what is involved in capturing a digital image of a real world scene: • Image sensing and representation • Sampling and quantisation • Resolution

  9. Image Sensing and Acquisition • A typical image formation system consists of an illumination” source, and a sensor. • Energy from the illumination source is either reflected or absorbed by the object or scene, which is then detected by the sensor. • Depending on the type of radiation used, a photo converter (e.g., a phosphor screen) is typically used to convert the energy into visible light. • Sensors that provide digital image as output, the incoming energy is transformed into a voltage waveform by a sensor material that is responsive to the particular energy radiation. • The voltage waveform is then digitized to obtain adiscreteoutput.

  10. Images

  11. Image Sensors • Incoming energy is transformed into a voltage by the combination of input electrical power and sensor material.

  12. Basic Concepts in Image Samplingand Quantization Continuous image to be converted into digital :form Sampling: digitize the coordinate values Quantization: digitize the amplitude values Issues in sampling and quantization, related to .sensors

  13. Representing digital images • Conventions • Origin at the top • left corner • x increases from left to right • y increases from top to bottom • Each element of the matrix array is called a pixel, for picture element

  14. Representing digital images(cont.) • Matrix form bits to store the image = M x N x k gray level = 2k

  15. Spatial and Gray-LevelResolution • L-level digital image of size MxN • = digital image having • a spatial resolution MxN pixels • a gray-level resolution of L levels • Spatial resolution determined by sampling • Smallest discernible detail in an image • Gray-level resolution determined by number of gray scales • Smallest change in gray level

  16. Multi-rate image processing • Down-sampling • Up-sampling

  17. Down-sampling operations

  18. See the information loss due to downsampling

  19. Gray-Level Reduction

  20. Gray-Level Reduction

  21. Empirical study of resolutions • 2k-level digital image of size NxN • How K and N affect the image quality

  22. Sampling and quantizationQuality • How many samples and gray levels are required for a good approximation? • Quality of an image depends on number of pixels and gray-level number • i.e. the more these parameters are increased, the closer the digitized array approximates the original image. • But: Storage & processing requirements increase rapidly as a function of N, M, and k

  23. Zoom and Shrink Operations applied to digital images: • Zoom: up-sampling • Pixel duplication • Bi-linear interpolation • Shrink: down-sampling

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