Chapter 1. Introduction

Goals of Image Processing “One picture is worth more than a thousand words” 1.Improvement of pictorial information for human interpretation. 2.Processing of scene data for autonomous machine perception.

Related Areas of Image Processing Image Processing: image  image Computer Graphics:information  image Computer Vision:image  information

1.Image Analysis 2.Image Restoration 3.Image Enhancement 4.Image Compression Applications of Image Processing

Example of Image Restoration

Example of Image Enhancement

Steps in Digital Image Processing

Digital Image

Sampling & Quantization

Sampling

Quantization False contours

Storage requirement A MxN image with 2 k gray scales # of storage bits = M x N x k

Example Generally, transmission is accomplished in packets consisting of a start bit, a byte of information, and a stop bit. Using this approach, how many seconds would it take to transmit a 1024x1024 image with 256 gray levels at 300 baud (bits/sec)?

Types of Images  Analog Image  Digital Image 1.Binary Image 2.Gray-scale Image 3.Color Image 4.Multispectral Image

Multispectral Image

Electromagnetic Spectrum

 Vector Image  Bitmap Image RAW  no header RLE (Run-Length Encoding) PGM,PPM,PNM (Portable Gray Map) GIF (Graphics Interchange Format)  no more than 256 colors TIF (Tag Image File Format)  Scanner EPS (Encapsulated Postscript)  Printer JPEG (Joint Photographic Experts Group)  Compression ratio MPEG (Motion Picture Experts Group)  Video Image Formats

Comparison of Image Formats

Human Visual Perception

Machine Visual Perception

Perception of objects 1.The spectrum (energy) of light source. 2.The spectral reflectance of the object surface. 3.The spectral sensitivity of the sensor (eye or camera).

Human eye

How do we see an object? Light Object Eye Luminance  Lightness  Rods Chrominance  Color  Cones Human eye is more sensitive to luminance than to chrominance

Cones & Rods ( day & night )

Three kinds of Cones

Brightness adaptation

Brightness illusions: Mach band effect

Contrast illusions

Geometric illusions

Spatial & Temporal Resolution Spatial resolution: 4-50 cycles per degree Spatial resolution: 4-50 cycles per degree Temporal resolution: 50 cycles per second Temporal resolution: 50 cycles per second Brightness resolution: 100 gray levels Brightness resolution: 100 gray levels

Color Spectrum

Electromagnetic spectrum

RGB Model

RGB signals from a video camera

Color measurement: A mixture of red, green, and blue light Values between 0.0 (none) and 1.0 (lots) Color examples Red Green Blue White Black Yellow Magenta Cyan RGB Model

rgb Model(Normalized RGB) r+g+b=1

Chromaticity Diagram

Typical color gamut

HSI Model

Color Complements

CMY Model

Light vs. Pigment

YIQ Model TV transmission  digital space  YC B C R  analog space  YIQ (NTSC)  YUV (PAL)

YUV & YC B C R Model

TV Broadcast