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Fundamentals of Digital Image Processing: Human Vision and Image Digitization Techniques

This chapter delves into the essentials of digital image processing, starting with the intricate workings of the human visual system. It explores how images are digitized and displayed, likening human perception to a multi-mega pixel camera. Key concepts include sampling, quantization, and convolution, alongside the response characteristics of the eyes. The text emphasizes practical implications, such as effective image displays. It also introduces essential filtering methods like blurring and median filtering, addressing their significance in noise reduction and image enhancement.

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Fundamentals of Digital Image Processing: Human Vision and Image Digitization Techniques

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Presentation Transcript

  1. Chapter 5 Digital Image Processing Fundamentals

  2. Learning Goals • The human visual system • Digitizing images • Display of Images

  3. Trading an eye for an ear

  4. An eye is a Multi-mega pixel camera • It has a lens (adjustable zoom) • It has an automatic f-stop (iris 2-8 mm) • It has a sensor plane (100 million pixels) • The sensor has a transfer function senstive to mesopic range; 380 to about 700 nm

  5. The eyes have a USB2 data rate! • 250,000 neurons in the optic nerve • variable voltage output on EACH nerve • 17.5 million neural samples per second • 12.8 bits per sample • 224 Mbps, per eye (a 1/2 G bps system!). • Compression using lateral inhibition between the retinal neurons

  6. Response curves • Eye has Gaussian response to light. • Gives rise to biologically motivated image processing

  7. Quantization of an Image • Computers use digital cameras -> quantization

  8. Delta-function

  9. Sampling an Image

  10. Sampling=convolution w pulse train

  11. Quantization Error is visible

  12. Displays • Color Monitors are made for people

  13. Chapter 6-7 • Opening and Saving Images

  14. Chapter 8 Convolutionwith a kernel, g(x)

  15. Convolution 1D and 2D signal processing

  16. Consider the delta function

  17. Time-shift delta

  18. Sample the input (it’s a convolution!)

  19. What does sampling do to spectrum?

  20. What is the spectrum?

  21. Fourier Coefficients

  22. CTFT

  23. Euler’s identity

  24. Sine-cos Rep

  25. Harmonic Analysis

  26. Convolution=time-shift&multi

  27. Convolution Thm multiplication in the time domain = convolution in the frequency domain

  28. Sample

  29. Spectrum reproduced spectrum to be reproduced at intervals

  30. Summary

  31. Example of 1D convoln

  32. 2D Convolution

  33. Region of Support • The region of support is defined as that area of the .kernel which is non-zero • linear convolution:=signal has infinite extent but kernel has finite support • If function has finite region of support we have compact support

  34. Real images have finite region of support • But we treat them as periodic and infinite! • We repeat kernels so that they have the same period as the images. • We call this cyclic convolution.

  35. Convolution in 2D

  36. Avoid the Mod op

  37. What is wrong with avoiding the mod op? • How do I find the center of the kernel?

  38. Cyclic Convolution

  39. Implementing Convolution for(int y = 0; y < height; y++) { for(int x = 0; x < width; x++) { sum = 0.0; for(int v = -vc; v <= vc; v++) for(int u = -uc; u <= uc; u++) sum += f[cx(x-u) ][cy(y-v)] * k[ u+uc][v+vc]; if (sum < 0) sum = 0; if (sum > 255) sum = 255; h[x][y] = (short)sum; } }

  40. What happens to the image if you ignore the wrap?

  41. Cyclic Convolution keeps the edges

  42. Can you think of a better way to implement convolution? • Keep the edges! • Don’t use the mod operation. • How about growing the image by the size of the kernel*2?

  43. Convolution is slow, how can I speed it up? • JAI! • FFT!? • Other ideas?

  44. Chapter 9 • Spatial Filters • Blurring • Median Filtering • Hi-pass filtering • SpatialFilterFrame and JAI

  45. Blurring=Low-pass Filters

  46. Why Blur an Image? • Remove Noise • Other ideas?

  47. Average=low-pass

  48. Want Unity Gain

  49. Many variations on LP filters

  50. Gaussian Blur

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