unit 1 a notes

DIP Unit I- Introduction to Digital Image Processing

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Malineni Perumallu Educational Society’s Group of Institutions, Guntur, Prepared by Ch Ramesh, HOD,ECE 1

Digital Image Processing

Lecture NotePrepared by –

Ch.RameshAssociate

Professor & HOD,ECE

MPES, Guntur


JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY: KAKINADAB.TECH. (ELECTRONICS AND COMMUNICATION ENGINEERING)

DIGITAL IMAGE PROCESSINGUNIT IIntroduction Origins of Digital Image Processing, Uses Digital Image Processing. Fundamental Steps in Digital Image Processing, Components of an Image Processing SystemDigital Image Fundamentals Elements of Visual Perception, Light and Electromagnetic Spectrum, Imaging Sensing and Acquisition, Image Sampling and Quantization, Some Basic relationships between Pixels. An Introduction to the Mathematical Tools used in Digital Image Processing. Image Transforms: Need for Image Transforms, Spatial Frequencies in Image Processing, Introduction to Fourier Transform, Discrete Fourier Transform and its algorithm, Properties of Fourier transform – Sampling Theorem, Parseval’s Theorem, Discrete Cosine Transform, Discrete Sine Transform, Walsh Transform, Hadamard Transform, Haar Transform, Slant Transform, SVD and KL transform or Hotelling transform.

UNIT IIIntensity Transformations and Spatial Filtering Background, Some Basic Intensity Transformation Functions, Histogram Processing, Fundamentals of Spatial Filtering, Smoothing Spatial Filters, Sharpening Spatial Filters, Combining Spatial Enhancement Methods, using Fuzzy techniques for Intensity Transformations and Spatial Filtering.

UNIT IIIFiltering in the Frequency Domain Preliminary Concepts, Sampling and the Fourier Transform of Sampled Functions, The Discrete Fourier Transform(DFT) of one Variable, Extension to Functions of Two variables, Some Properties of the 2-D Discrete Fourier Transform, The Basic of Filtering in the Frequency Domain, Image Smoothing using Frequency Domain Filters, Selective Filtering, Implementation.

UNIT IVImage Restoration and Reconstruction A Model of the Image Degradation/ Restoration Process, Noise Models, Restoration in the Presence of Noise Only- Spatial Filtering, Periodic Noise Reduction by Frequency Domain Filtering, Linear, Position-Invariant Degradations, Estimation the Degradation Function, Inverse Filtering Minimum Mean Square Error(Wiener) Filtering, Constrained Least Squares Filtering, Geometric Mean Filter, Image Reconstruction from Projections

UNIT VColor Image Processing Color Fundamentals, Color Models, Pseudocolor Image Processing, Basic of Full-Color Image Processing, Color Transformations, Smoothing and Sharpening, Image Segmentation Based on Color, Noise in Color Images, Color Image Compression

UNIT VIWavelets and Multi-resolution ProcessingImage Pyramids, Subband Coding & Haar Transform, Multiresolution Expansions, Wavelet Transform in one Dimension, The Fast Wavelet Transform, Wavelet Transforms in Two Dimensions, Wavelet packetsImage CompressionFundamentals, Various Compression methods – Coding Techniques, Digital Image Watermarking

UNIT VIIMorphological Image Processing Preliminaries, Erosion and Dilation, Opening and Closing, The Hit-or-Miss Transformation, Some Basic Morphological Algorithms, Grey-Scale Morphology. UNIT VIIIImage Segmentation Fundamentals, Point, Line and Edge Detection, Thresholding, Region-Based Segmentation, Segmentation Using Morphological Watersheds. The use of Motion in Segmentation.

TEXT BOOKS :

1. Rafael C. Gonzalez and Richard E.Woods, “Digital Image Processing” Pearson Education,20112. S.Sridhar, “ Digital Image Processing” Oxford Publishers, 2011

REFERENCES : 1. .S.Jayaraman, S.Esakkirajan, T. Veerakumar, “Digital Image Processing” Mc Graw Hill Publishers, 2009

2. B.Chandra and D. Dutta Majumber, “ Digital Image Processing and Analysis” Prentice Hall of India, 2011/2012

3. Anil K. Jain, “ Fundamentals of Digital Image Processing,” Prentice Hall of India, 2012 4.Millan Sonka, Hlavac & Boyle, “ Digital Image Processing and Computer Vision”, Cengage Learning Publishers, 2010



What is Digital Image Processing ?

Why Digital Image Processing ?




Early 1929s:The early Bartlane systems were capable of coding images in five distinct levels of gray.This capability was increased to 15 levels in 1929.Figure 1.3 is typical of the type of images that could be obtained using the 15-tone equipment.

The Origins of DIP?



The invention of the transistor at Bell Laboratories in 1948 The development in the 1950s and 1960s if the high level programming languages COBOL (Common Business Oriented Language) and FORTAN (Formula Translator). The invention of the integrated circuit (IC) at Texas instruments in 1958. The development of the operating systems in the early 1960s The development of microprocessor by Intel in the early 1970s Progressive miniaturization of components, starting with large scale integration (LI) in the late 1970s, then VLSI in the 1980s to the present use of ultra large scale integration (ULSI).

The Origins of DIP?



The Origins of DIP?1960s: The first computers

powerful enough to carry out meaningful image processing tasks appeared in the early 1960s.1964s: Work on computers techniques for improving images for space probe began at the Jet Propulsion Laboratory (California) in 1964 Fig 1.4 shows the first image of the moon taken by Ranger 7 on July 31, 1964 . This is also the first image of the moon taken by a U.S space craft.



1970s: Digital image processing begins to be used in medical applications

1979: Sir Godfrey N. Hounsfield & Prof. Allan M. Cormack share the Nobel Prize in medicine for the invention of tomography, the technology behind Computerised Axial Tomography (CAT) scans

Typical head slice CAT image

The Origins of DIP?



1980s - Today: The use of digital image processing techniques has exploded and they are now used for all kinds of tasks in all kinds of areasImage enhancement/restorationArtistic effectsMedical visualisationIndustrial inspectionLaw enforcementHuman computer interfaces

The Origins of DIP?



Step 7: Morphological ProcessingTools for extracting image components that are useful in the representation and description of shape. In this step, there would be a transition from processes that output images, to processes that output image attributes.

Step 8: Image SegmentationSegmentation procedures partition an image into its constituent parts or objects.



Step 9: Representation and Description

Representation: Make a decision whether the data should be represented as a boundary or as a complete region. It is almost always follows the output of a segmentation stage.Boundary Representation: Focus on external shape characteristics, such as corners and inflections Region Representation: Focus on internal properties, such as texture or skeleton shape

Step 9: Representation and Description

Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing (mainly recognition)

- Description: also called, feature selection, deals with extracting attributes that result in some information of interest.



Step 9: Recognition and InterpretationRecognition: the process that assigns label to an object based on the information provided by its description.

Step 10: Knowledge BaseKnowledge about a problem domain is coded into an image processing system in the form of a knowledge database.



Components of general purpose image processing

system

unit 1 a notes

Documents

pseudocolor image processing

image sampling

image transforms

image smoothing

image reconstruction

unit iv image restoration

histogram processing

dip unit