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Image Signal Processing

Image Signal Processing. Instructor: Prof. A. N. Rajagopalan, Department of Electrical and Electronics Engineering, IIT Madras. This course spans both basics and advances in digital image processing. Starting from image formation in pin-hole and lens based cameras, it goes on to discuss geometric transformations and image homographies, a variety of unitary image transforms, several image enhancement methods, techniques for restoration of degraded images, and 3D shape recovery from images. (from nptel.ac.in)

Lecture 04 - Basics of Images


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Lecture 01 - Course Introduction
Lecture 02 - Applications of Image Processing
Lecture 03 - Applications of Image Processing (cont.)
Lecture 04 - Basics of Images
Lecture 05 - Shot Noise
Lecture 06 - Geometric Transformations
Lecture 07 - Geometric Transformations (cont.)
Lecture 08 - Bilinear Interpolation
Lecture 09 - Geometric Transformations (cont.)
Lecture 10 - Projective Transformation
Lecture 11 - Homography
Lecture 12 - Homography: Special Cases
Lecture 13 - Computing Homography
Lecture 14 - RANSAC
Lecture 15 - Rotational Homography
Lecture 16 - Research Challenges
Lecture 17 - Real Aperture Camera
Lecture 18 - Real Aperture Camera: Introduction
Lecture 19 - Circle of Confusion
Lecture 20 - Depth of Field, Linearity
Lecture 21 - Space Invariance
Lecture 22 - 2D Convolution
Lecture 23 - 2D Convolution (cont.)
Lecture 24 - Blur Models
Lecture 25 - Space-Variant Blurring
Lecture 26 - Shape from X: Introduction
Lecture 27 - 2-View Stereo
Lecture 28 - Introduction to Shape from Focus
Lecture 29 - SFF Principle
Lecture 30 - Shape from Focus: Gaussian Fitting
Lecture 31 - Shape from Focus: Focus Operators
Lecture 32 - Shape from Focus: Examples
Lecture 33 - Shape from Focus: Tensor Voting
Lecture 34 - DFD Principle
Lecture 35 - Motion Blur
Lecture 36 - Image Transforms: Introduction
Lecture 37 - Image Transforms: Motivation
Lecture 38 - 1D Unitary Transforms: Introduction
Lecture 39 - Extending 1D Unitary Transforms to 2D: Motivation
Lecture 40 - Extending 1D Unitary Transforms to 2D: Example
Lecture 41 - Alternative Forms of 2D
Lecture 42 - Kronecker Product
Lecture 43 - Kronecker Product (Example Revisited)
Lecture 44 - Extending 1D Unitary Transforms to 2D: Summary
Lecture 45 - 1D DFT to 2D DFT
Lecture 46 - 2D DFT Visualization
Lecture 47 - 2D DFT Computation
Lecture 48 - 1D DCT: Definition, Motivation
Lecture 49 - Relation to DFT
Lecture 50 - 2D DCT and Walsh-Hadamard Transform
Lecture 51 - Data Dependent Transforms, Karhunen Loeve Transform
Lecture 52 - Karhunen Loeve Transform: Concept
Lecture 53 - Karhunen Loeve Transform: Applications
Lecture 54 - Karhunen Loeve Transform: Applications (cont.)
Lecture 55 - Singular Value Decomposition (SVD)
Lecture 56 - Applications of Singular Value Decomposition (SVD)
Lecture 57 - Change Detection
Lecture 58 - Image Thresholding
Lecture 59 - Adaptive Local Thresholding: Motivation
Lecture 60 - Chow-Kaneko Local Thresholding
Lecture 61 - K-Means Method
Lecture 62 - ISODATA Method
Lecture 63 - Theory of Histogram Equalization and Modification
Lecture 64 - Histogram Equalization Example
Lecture 65 - Image Sequence and Single Image Filtering in Gaussian Noise
Lecture 66 - Non-Local Means Method
Lecture 67 - Non-Local Means Filtering (Examples)
Lecture 68 - Impulse Noise Generator
Lecture 69 - Impulse Noise Filtering
Lecture 70 - Transform Domain Filtering
Lecture 71 - Illumination Handling
Lecture 72 - Applications of Restoration, and Image Deblurring
Lecture 73 - Haddamard's Conditions and Least Squares Solution
Lecture 74 - Min-Norm Solution and Norm of Linear Operator
Lecture 75 - Numerical Stability Analysis
Lecture 76 - Image Blurring
Lecture 77 - Tikhonov-Miller Regularization
Lecture 78 - Conditional Mean as an Estimator
Lecture 79 - Linear Estimator
Lecture 80 - Wiener Filter
Lecture 81 - Fourier Wiener Filter
Lecture 82 - 1D Superresolution
Lecture 83 - Superresolution Examples