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Digital Image Processing SCSVMV Dept of ECE DIGITAL IMAGE PROCESSING LECTURE NOTES B.E (IVYEAR) Prepared by Dr.S.Vijayaraghavan Assistant Professor-ECE SCSVMV Deemed University, Kanchipuram Page | 1 Digital Image Processing SCSVMV Dept of ECE DIGITAL IMAGE PROCESSING VII-Semester Pre-requisite: Basic knowledge of Signals & L T P C Systems, Digital Signal Processing and Digital 4 1 0 4 Design OBJECTIVES: ➢ To learn digital image fundamentals. ➢ To be exposed to simple image processing techniques. ➢ To be familiar with image compression and segmentation techniques ➢ To represent image in form of features. UNIT - I DIGITAL IMAGE FUNDAMENTALS Introduction – Origin – Steps in Digital Image Processing – Components – Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels - color models. UNIT - II IMAGE ENHANCEMENT Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–Smoothing and Sharpening Spatial Filtering – Frequency Domain: Introduction to Fourier Transform– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters. UNIT - III IMAGE RESTORATION AND SEGMENTATION Noise models – Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering Segmentation: Detection of Discontinuities– Edge Linking and Boundary detection – Region based segmentation- Morphological processing- erosion and dilation. Page | 2 Digital Image Processing SCSVMV Dept of ECE UNIT - IV WAVELETS AND IMAGE COMPRESSION Wavelets – Sub band coding – Multi-resolution expansions - Compression: Fundamentals – Image Compression models – Error Free Compression – Variable Length Coding – Bit-Plane Coding – Lossless Predictive Coding – Lossy Compression – Lossy Predictive Coding – Compression Standards. UNIT - V IMAGE REPRESENTATION AND RECOGNITION Boundary representation – Chain Code – Polygonal approximation, signature, boundary segments – Boundary description – Shape number – Fourier Descriptor, moments- Regional Descriptors – Topological feature, Texture - Patterns and Pattern classes - Recognition based on matching. OUTCOMES: At the end of the course, the student should be able to: ✓ Understand the image enhancement techniques ✓ Understand the concept of restoration and segmentation ✓ Understand wavelets and image compression TEXT BOOK: 1. Rafael C. Gonzales, Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson Education, 2010. REFERENCES: 1. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using MATLAB”, Third Edition Tata Mc Graw Hill Pvt. Ltd., 2011. 2. Anil Jain K. “Fundamentals of Digital Image Processing”, PHI Learning Pvt. Ltd., 2011. 3. Willliam K Pratt, “Digital Image Processing”, John Willey, 2002. 4. Malay K. Pakhira, “Digital Image Processing and Pattern Recognition”, First Edition, PHI Learning Pvt. Ltd., 2011 Page | 3 Digital Image Processing SCSVMV Dept of ECE UNIT-1 DIGITAL IMAGE FUNDAMENTALS LEARNING OBJECTIVES: This unit provides an overview of the image –processing system which includes various elements like image sampling, quantization, Basic steps in image processing, image formation, storage and display. After completing this unit, the reader is expected to be familiar with the following concepts: 1. Image sampling 2. Image sensors 3. Different steps in image processing 4. Image formation DIGITAL IMAGE FUNDAMENTALS: The field of digital image processing refers to processing digital images by means of digital computer. Digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are called picture elements, image elements, pels and pixels. Pixel is the term used most widely to denote the elements of digitalimage. An image is a two-dimensional function that represents a measure of some characteristic such as brightness or color of a viewed scene. An image is a projection of a 3- D scene into a 2D projection plane. Page | 4
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