Final image processing

Digital Image processing

Introduction

“One picture is worth more than ten thousand words”

Anonymous

Contents

– What is a digital image?– What is digital image processing?– Key stages in digital image processing.– Art examples of digital image processing.– Image Interpolation.– Techniques of image interpolation.– Summary

What is a Digital Image?

A digital image is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels.

What is a Digital Image?

Common image formats include:– 1 sample per point (B&W or Grayscale)– 3 samples per point (Red, Green, and Blue)– 4 samples per point (Red, Green, Blue, and “Alpha”,

a.k.a. Opacity)

What is Digital Image Processing?

Digital image processing focuses on two major tasks

– Improvement of pictorial information for human interpretation

– Processing of image data for storage, transmission and representation for autonomous machine perception

Key Stages in Digital Image Processing

Image Acquisition

Image Restoration

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Image Restoration

Image Acquisition

Image Restoration

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Segmentation

Image Acquisition

Image Restoration

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Object Recognition

Image Acquisition

Image Restoration

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Representation & Description

Image Acquisition

Image Restoration

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Image Compression

Image Acquisition

Image Restoration

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Colour Image Processing

Image Acquisition

Image Restoration

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Image Acquisition

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Key Stages in Digital Image Processing:Image Enhancement

Image Acquisition

Image Restoration

Morphological Processing

Segmentation

Representation & Description

Image Enhancement

Object Recognition

Problem Domain

Colour Image Processing

Image Compression

Examples: Image Enhancement

One of the most common uses of DIP techniques: improve quality, remove noise etc

Examples: Artistic Effects

Artistic effects are used to make images more visually appealing, to add special effects and to make composite images

Examples: Medicine

Take slice from MRI scan of canine heart, and find boundaries between types of tissue

– Image with gray levels representing tissue density

– Use a suitable filter to highlight edges

Original MRI Image of a Dog Heart Edge Detection Image

Examples: Industrial Inspection

•Human operators are expensive, slow andunreliable.

•Make machines do thejob instead.

•Industrial vision systems .

Examples: Law Enforcement

Image processing techniques are used extensively by law enforcers

– Number plate recognition for speed cameras/automated toll systems

– Fingerprint recognition– Enhancement of

CCTV images

Examples: HCI

Try to make human computer interfaces more natural

– Face recognition– Gesture recognition

Does anyone remember the user interface from “Minority Report”?

These tasks can be extremely difficult

22

Image interpolation

• What is image interpolation?– An image f(x,y) tells us the intensity values at the

integral lattice locations, i.e., when x and y are both integers

– Image interpolation refers to the “guess” of intensity values at missing locations, i.e., x and y can be arbitrary

– Note that it is just a guess (Note that all sensors have finite sampling distance)

24

Engineering Motivations

• Why do we need image interpolation?– We want BIG images

• When we see a video clip on a PC, we like to see it in the full screen mode

– We want GOOD images• If some block of an image gets damaged during the

transmission, we want to repair it– We want COOL images

• Manipulate images digitally can render fancy artistic effects as we often see in movies

Techniques of interpolation

•Image quality highly depends on the used interpolation techniques.•The techniques used for interpolation are:

1.Nearest neighbor2.Linear interpolation3.Cubic interpolation4.B-splines

Nearest neighbor

The simplest interpolation from a computational standpoint

•The simplest interpolation from a computational standpoint.

• Here each interpolated output pixel is assigned the value of the nearest sample point in the input image.

•This technique is also known as point shioft algorithm and pixel replication.

•The interpolation kernel for the nearest neighbor algorithm is defined as

•Frequency response of the nearest neighbor kernel is:

•Kernel and its fourier transform is given in figure as:

•This technique achieves magnification by pixel replication, by sparse point sampling. For large-scale changes, nearest neighbor interpolation produces images with blocky effects.

Linear Interpolation

•Linear interpolation is a first degree method that passes a straight line through every two consucutive points of the input signal.•In the spatial domain, linear interpolation is equivalent to convolving the sampled input with the following kernel.

• Frequency response of linear interpolation is :

•This kernel is also called triangle filter, roof function or Bartlett windoe.

•The frequency response of the linear interpolation kernel is superior to that of the nearest neighbor interpolation function.

•The side lobes are less prominent, so the performance is improved in the stopband.• A passband is moderately attenuated, resulting in image smoothing.

Cubic convolution

•Cubic convolution is a third degree interpolation algorithm that fairly well approximates the theoretically optimum sinc interpolation function.•The kernel is composed of piecewise cubic polynomials defined on subintervals (-2, -1), (-1, 0), (0, 1) and (1, 2).•The kernel is of form:

•The frequency response is:

•Choices for a are a=-1, a=-0.75 and a=-0.5. •The performance of the interpolation kernel depends on a, and the frequency content of the image.

• For different images, different values of the parameter a gives the best performance.

B-splines•A B-spline of degree n is derived through n convolutions of the box filter, B0 ..

•The cubic B-spline B3 is generated from convolving B0*B2. That is B3=B0*B0*B0*B0 .•The cubic B-spline interpolation kernel is defined as:

•Unlike cubic convolution, the cubic B-spline kernel is not interpolatory since it does not satisfy the necesssary constraint that h(0)=1 and h(1)=h(2)=0.

•Figure summarizes the shapes of these low-order B-splines.

•The positivity of the B-spline kernel is attractive for our image processing application. When using kernels with negative lo obes, it is possible to generate negative values while interpolating positive data.

34

Summary of Image processing

• Image processing has anenormous range of applications; almost every area of science and technology can make use of its methods.

• Wide applications from consumer electronics to biomedical imaging

• Interpolation is a hot topic after the IT bubbles break

• A fundamental tool in digital processing of images: bridging the continuous world and the discrete world.