Image Signal Processing

Image Signal Processing Training provided by University of Indian Institute of Technology Madras

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Created by IIT Madras Staff Last updated Mon, 28-Feb-2022 English


Image Signal Processing free videos and free material uploaded by IIT Madras Staff .

Syllabus / What will i learn?

Week 1: Introduction to Image Processing, Basics of Imaging, Geometric Transformations

Week 2: Hierarchy of Transformations, Rotational Representation, Homography Computation

Week 3: Research Challenges Involving Camera Motion, Basics of Real Aperture Camera, Lens as LSI System

Week 4: Blur Kernels, Shape from X, Shape from Focus

Week 5: Shape from Focus, Generalized Shape from Focus, Depth from Defocus (DFD) and Motion Blur

Week 6: Unitary Image Transforms, From 1D to 2D Unitary Transforms, Higher Dimensional Unitary Transforms

Week 7: 2D Unitary Transforms, 2D Discrete Fourier Transform, 2D Discrete Cosine Transform

Week 8: Karhunen-Loeve Transform (KLT), Applications of KLT, Singular Value Decomposition

Week 9: Image Enhancement, Adaptive Thresholding, K-Means Clustering, ISODATA Clustering

Week 10:Contrast Stretching, Noise Filtering, Non-local Mean Filtering, Impulse Noise Filtering, Noise Filtering in Transform Domain, Illumination Compensation

Week 11:Image Restoration, Ill-posed Problems, Matrix Conditioning, Matrix Numerical Stability, Inverse filter for Image Deblurring, Regularization Theory

Week 12:Minimum Mean Square Error (MMSE) Estimator, Linear MMSE, Spatial Wiener Filter, Wiener filter in Fourier domain, Image Super-resolution, Super-resolution Examples



Curriculum for this course
0 Lessons 00:00:00 Hours
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Description

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 learnersPREREQUISITES :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.

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Free

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