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ECE 637: Digital Image Processing I (Spring 2011, Purdue University). Instructor: Professor Charles A. Bouman.

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September 25, 2023

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September 25, 2023

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FREE

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Description

ECE 637: Digital Image Processing I (Spring 2011, Purdue University). Instructor: Professor Charles A. Bouman.

Introduction to digital image processing techniques for enhancement, compression, restoration, reconstruction, and analysis. Lecture and laboratory experiments covering a wide range of topics including 2-D signals and systems, image analysis, image segmentation; achromatic vision, color image processing, color image systems, image sharpening, interpolation, decimation, linear and nonlinear filtering, printing and display of images; image compression, image restoration, and tomography. (from engineering.purdue.edu)

Course Curriculum

  • Lecture 01 – Introduction Unlimited
  • Lecture 02 – Continuous Time Fourier Transform and Continuous Space Fourier Transform Unlimited
  • Lecture 03 – CSFT and Rep and Comb Relations Unlimited
  • Lecture 04 – Optical Image Systems Unlimited
  • Lecture 05 – Helical Scan Multislice CT and PET Unlimited
  • Lecture 06 – Tomographic Reconstruction: Fourier Slice Theorem and Filtered Back Projection Unlimited
  • Lecture 07 – FBP (Filtered Back Projection) and Magnetic Resonance Imaging (MRI) Unlimited
  • Lecture 08 – MRI Reconstruction Unlimited
  • Lecture 09 – MRI and C-Programming Unlimited
  • Lecture 10 – C-Programming Unlimited
  • Lecture 11 – DTFT, DSFT, Sampling, and Reconstruction Unlimited
  • Lecture 12 – 2-D Reconstruction and Focal Plane Arrays Unlimited
  • Lecture 13 – Sampling and Reconstruction for Focal Plane Arrays Unlimited
  • Lecture 14 – FIR and IIR Filters Unlimited
  • Lecture 15 – IIR Filters and Random Variables Unlimited
  • Lecture 16 – Random Variables and Random Processes Unlimited
  • Lecture 18 – Power Spectral Density and AR Processes Unlimited
  • Lecture 19 – Eigen Signal Analysis Unlimited
  • Lecture 20 – Eigen Signal Analysis and Edge Detection Unlimited
  • Lecture 21 – Edge Detection and Connected Component Analysis Unlimited
  • Lecture 22 – Segmentation, Clustering, and Color Vision Illusions Unlimited
  • Lecture 23 – Achromatic Vision Unlimited
  • Lecture 24 – Contrast, CSF (Contrast Sensitivity Function), and Achromatic Image Quality Metrics Unlimited
  • Lecture 25 – Color Matching Functions Unlimited
  • Lecture 26 – Color Matching Functions and Subtractive Color Systems Unlimited
  • Lecture 27 – Subtractive Color Systems, Chromaticity Diagrams Unlimited
  • Lecture 28 – Chromaticity Diagrams and White Point Unlimited
  • Lecture 29 – White Point, Color Transforms, and sRGB Unlimited
  • Lecture 30 – More Color Transforms Unlimited
  • Lecture 31 – More Quality Metrics and Rate Conversion Unlimited
  • Lecture 32 – Rate Conversion Unlimited
  • Lecture 33 – Rate Conversion and Image Restoration Unlimited
  • Lecture 34 – Image Restoration and Nonlinear Filtering Unlimited
  • Lecture 36 – Nonlinear Filtering and M-Estimators Unlimited
  • Lecture 37 – Halftoning and Ordered Dither Unlimited
  • Lecture 38 – Error Diffusion Unlimited
  • Lecture 39 – Error Diffusion and RAPS Unlimited
  • Lecture 40 – Entropy and Source Coding Unlimited
  • Lecture 41 – Entropy and Source Coding Unlimited
  • Lecture 42 – Lossy Source Coding and Rate-Distortion Theory Unlimited
  • Lecture 43 – Rate-Distortion Theory Unlimited
  • Lecture 44 – JPEG Image Coding Unlimited

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