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Online Course

Image Signal Processing

Indian Institute of Technology Madras and NPTEL via Swayam

Overview

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.


INTENDED AUDIENCE :
Any interested learners
PREREQUISITES :Digital Signal Processing. Familiarity with linear algebra and probability theory is desirable.
INDUSTRIES SUPPORT :Google, Amazon, Facebook, Microsoft, KLA-Tencor, Qualcomm, Intel, Analog Devices, Philips, GE, Siemens and many more.

Syllabus

COURSE LAYOUT

Week 1: Introduction to Image Processing, Basics of Imaging, Geometric TransformationsWeek 2: Hierarchy of Transformations, Rotational Representation, Homography ComputationWeek 3: Research Challenges Involving Camera Motion, Basics of Real Aperture Camera, Lens as LSI SystemWeek 4: Blur Kernels, Shape from X, Shape from FocusWeek 5: Shape from Focus, Generalized Shape from Focus, Depth from Defocus (DFD) and Motion BlurWeek 6: Unitary Image Transforms, From 1D to 2D Unitary Transforms, Higher Dimensional Unitary TransformsWeek 7: 2D Unitary Transforms, 2D Discrete Fourier Transform, 2D Discrete Cosine TransformWeek 8: Karhunen-Loeve Transform (KLT), Applications of KLT, Singular Value DecompositionWeek 9: Image Enhancement, Adaptive Thresholding, K-Means Clustering, ISODATA ClusteringWeek 10:Contrast Stretching, Noise Filtering, Non-local Mean Filtering, Impulse Noise Filtering, Noise Filtering in Transform Domain, Illumination CompensationWeek 11:Image Restoration, Ill-posed Problems, Matrix Conditioning, Matrix Numerical Stability, Inverse filter for Image Deblurring, Regularization TheoryWeek 12:Minimum Mean Square Error (MMSE) Estimator, Linear MMSE, Spatial Wiener Filter, Wiener filter in Fourier domain, Image Super-resolution, Super-resolution Examples

Taught by

Prof. A.N. Rajagopalan

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