Image processing Presentation

Image Processing

Ref: Digital image processing ,Gonzalez & Woods

Components of an Image Processing System:


Image sensing

1. a physical device that is sensitive to the energy radiated by the object.

2. a digitizer

Technologies used1. Photo chemical, eg: photographic film 2. Photo electronic

The devices used 1. Still camera2. TV camera3. X ray scanner4. Radar5. Magnetic resonance imaging (MRI) system.

Digitizer:


Commonly used digitizers

1. Microdensitometer and

2. flying spot scanner.

Digital storage:


categorized as(1) short-term storage for use during processing, eg: frame buffers.(2) on-line storage for relatively fast re-call, and (3) archival storage, characterized by infrequent access.

Image displays:


Principal display devices are 1. printers, 2.TV monitor, 3. CRTs

Hardcopy:

laser printers, film cameras, heat-sensitive devices, inkjet units, and digital units, such as optical and CD-ROM disks, paper etc.

Structure of the Human Eye

Image Formation

Sampling and Quantization


Spatial and Gray-Level Resolution:

Spatial Resolution: is the smallest discernible detail in an image.Gray—level resolution: refers to the smallest discernible change in gray level .-subjective process.


Aliasing and Moiré Patterns:


Zooming and Shrinking Digital Images:

Zooming 1. creation of new pixel locations2. assignment of gray levels to those new locations.

1.Nearest neighbor interpolation2. Pixel replication3. Bilinear interpolation

v(x', y') = ax' + by' + cx'y' + d a= f (1, 0)- f (0, 0)b= f (0, 1)- f (0, 0)c= f (0, 0)-f (1, 0)- f (0, 1)+ f (1, 1)d= f (0, 0)

Image shrinking


2D bilenear interpolation

Original Before After No Interpolation

Basic Point Processing

Power-law transformations

Gamma Correction

Gamma Measuring Applet: http://www.cs.berkeley.edu/~efros/java/gamma/gamma.html

http://www.cs.berkeley.edu/~efros/java/gamma/gamma.html

http://www.cs.berkeley.edu/~efros/java/gamma/gamma.html

Image Enhancement

Contrast Streching

Histogram Equalization


Periodic noise

Mean and variance of the gray levels in image strips


Wrong sign for Q


Max and Min filter


Alpha trimmed mean filter with d=5


Adaptive noise reduction filtering

What is an image?

We can think of an image as a function, f, from R2 to R:• f( x, y ) gives the intensity at position ( x, y )

• Realistically, we expect the image only to be defined over a rectangle, with a finite range:

– f: [a,b]x[c,d] [0,1]

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

( , )

( , ) ( , )

( , )

r x y

f x y g x y

b x y

Images as functions

What is a digital image?

We usually operate on digital (discrete) images:• Sample the 2D space on a regular grid• Quantize each sample (round to nearest integer)

If our samples are apart, we can write this as:

f[i ,j] = Quantize{ f(i , j ) }

The image can now be represented as a matrix of integer values

Image processing

An image processing operation typically defines a new image g in terms of an existing image f.

We can transform either the range of f.

Or the domain of f:

What kinds of operations can each perform?

Point Processing

The simplest kind of range transformations are these independent of position x,y:

g = t(f)

This is called point processing.

What can they do?

What’s the form of t?

Important: every pixel for himself – spatial information completely lost!

Image Histograms

Cumulative Histograms

Histogram Matching

Match-histogram code

Neighborhood Processing (filtering)

Q: What happens if I reshuffle all pixels within the image?

A: It’s histogram won’t change. No point processing will be affected…

Need spatial information to capture this.

Programming Assignment #1

Easy stuff to get you started with Matlab• James will hold tutorial this week

Distance Functions• SSD• Normalized Correlation

Bells and Whistles• Point Processing (color?)• Neighborhood Processing• Using your data (3 copies!)• Using your data (other images)

Image processing Presentation

Education

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