Lecture Digital image processing: Introduction - Nguyễn Công Phương

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Lecture Digital image processing include the content: Introduction to Image processing and matlab; image acquisition, types, and file I/O; image arithmetic; spatial and frequency domain filter design; mage restoration and blind deconvolution;...

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Nội dung Text: Lecture Digital image processing: Introduction - Nguyễn Công Phương

  1. Nguyễn Công Phương DIGITAL IMAGE PROCESSING Introduction
  2. Contents I. Introduction to Image Processing & Matlab II. Image Acquisition, Types, & File I/O III. Image Arithmetic IV. Affine & Logical Operations, Distortions, & Noise in Images V. Image Transform VI. Spatial & Frequency Domain Filter Design VII. Image Restoration & Blind Deconvolution VIII. Image Compression IX. Edge Detection X. Binary Image Processing XI. Image Encryption & Watermarking XII. Image Classification & Segmentation XIII. Image – Based Object Tracking XIV. Face Recognition XV. Soft Computing in Image Processing 2
  3. References [1] A. Das. Guide to Signals and Patterns in Image Processing: Foundation, Methods and Application. Springer, 2015. [2] W. K. Pratt. Introduction to Digital Image Processing. CRC Press, 2014. [3] U. Qidwai, C. H. Chen. Digital Image Processing: An Algorithmic Approach with MATLAB®. CRC Press, 2009. [4] L. P. Yaroslavsky. Theoretical Foundations of Digital Imaging using MATLAB®. CRC Press, 2013. [5] 3
  4. Introduction 1. What is an Image? 2. Image Properties 3. Why Matlab? 4
  5. What is an Image? (1) • A digital image is a 2D y signal. • An image is a function (e.g., brightness) of two real variable a(x,y): – a: amplitude, real number or integer, – (x,y): real coordinate position. • Pixel: picture element. 0 x • Region of interest (ROI). 5
  6. What is an Image? (2) 6
  7. Image Properties (1) • Signal – to – Noise Ratio (SNR): signal _ power SNR  noise _ power • Image bit resolution (or image resolution): the number of grayscale level or the number of pixels present in the image. 7
  8. Image Properties (2) 8
  9. Image Properties (3) 9
  10. Why Matlab? • Images can be treated as matrices. • Matlab is optimized for matrix operations. • So … 10



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