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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 44 - Extending 1D Unitary Transforms to 2D: Summary

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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