By Mary Hudachek-Buswell. Overview Atmospheric Turbulence Blur.

Slides:



Advertisements
Similar presentations
Matrices A matrix is a rectangular array of quantities (numbers, expressions or function), arranged in m rows and n columns x 3y.
Advertisements

Solving Linear Systems (Numerical Recipes, Chap 2)
1.7 Diagonal, Triangular, and Symmetric Matrices.
Matrices & Systems of Linear Equations
Maths for Computer Graphics
CSCI 317 Mike Heroux1 Sparse Matrix Computations CSCI 317 Mike Heroux.
Matrices. Special Matrices Matrix Addition and Subtraction Example.
Sparse Matrix Algorithms CS 524 – High-Performance Computing.
Matrix Operations. Matrix Notation Example Equality of Matrices.
Chapter 2 Matrices Definition of a matrix.
Ch 7.2: Review of Matrices For theoretical and computation reasons, we review results of matrix theory in this section and the next. A matrix A is an m.
1 Neural Nets Applications Vectors and Matrices. 2/27 Outline 1. Definition of Vectors 2. Operations on Vectors 3. Linear Dependence of Vectors 4. Definition.
Monica Garika Chandana Guduru. METHODS TO SOLVE LINEAR SYSTEMS Direct methods Gaussian elimination method LU method for factorization Simplex method of.
Image deblurring Seminar inverse problems April 18th 2007 Willem Dijkstra.
Pam Perlich Urban Planning 5/6020
資訊科學數學11 : Linear Equation and Matrices
Linear regression models in matrix terms. The regression function in matrix terms.
Finding the Inverse of a Matrix
Chapter 5 Determinants.
Arithmetic Operations on Matrices. 1. Definition of Matrix 2. Column, Row and Square Matrix 3. Addition and Subtraction of Matrices 4. Multiplying Row.
CE 311 K - Introduction to Computer Methods Daene C. McKinney
Week 4 - Monday.  What did we talk about last time?  Vectors.
1 Chapter 2 Matrices Matrices provide an orderly way of arranging values or functions to enhance the analysis of systems in a systematic manner. Their.
Chapter 7 Matrix Mathematics Matrix Operations Copyright © The McGraw-Hill Companies, Inc. Permission required for reproduction or display.
1.7 Diagonal, Triangular, and Symmetric Matrices 1.
Today Wrap up of probability Vectors, Matrices. Calculus
Elementary Linear Algebra Howard Anton Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved. Chapter 1.
Elementary Linear Algebra Howard Anton Copyright © 2010 by John Wiley & Sons, Inc. All rights reserved. Chapter 1.
ECON 1150 Matrix Operations Special Matrices
1 Iterative Solution Methods Starts with an initial approximation for the solution vector (x 0 ) At each iteration updates the x vector by using the sytem.
Overview Definitions Basic matrix operations (+, -, x) Determinants and inverses.
2.5 Determinants and Multiplicative Inverses of Matrices Objectives: Evaluate determinants. Find inverses of matrices. Solve systems of equations by using.
Matrices. Definitions  A matrix is an m x n array of scalars, arranged conceptually as m rows and n columns.  m is referred to as the row dimension.
Matrix Algebra and Regression a matrix is a rectangular array of elements m=#rows, n=#columns  m x n a single value is called a ‘scalar’ a single row.
Linear Algebra 1.Basic concepts 2.Matrix operations.
Introduction to Matrices Douglas N. Greve
Review of Matrix Operations Vector: a sequence of elements (the order is important) e.g., x = (2, 1) denotes a vector length = sqrt(2*2+1*1) orientation.
MATRICES Operations with Matrices Properties of Matrix Operations
2.5 – Determinants and Multiplicative Inverses of Matrices.
CS 450: COMPUTER GRAPHICS TRANSFORMATIONS SPRING 2015 DR. MICHAEL J. REALE.
Section 2.1 Determinants by Cofactor Expansion. THE DETERMINANT Recall from algebra, that the function f (x) = x 2 is a function from the real numbers.
Matrix Algebra Basics Chapter 3 Section 5. Algebra.
Linear Algebra Engineering Mathematics-I. Linear Systems in Two Unknowns Engineering Mathematics-I.
Matrices. Variety of engineering problems lead to the need to solve systems of linear equations matrixcolumn vectors.
10.4 Matrix Algebra. 1. Matrix Notation A matrix is an array of numbers. Definition Definition: The Dimension of a matrix is m x n “m by n” where m =
2.1 Matrix Operations 2. Matrix Algebra. j -th column i -th row Diagonal entries Diagonal matrix : a square matrix whose nondiagonal entries are zero.
College Algebra Chapter 6 Matrices and Determinants and Applications
MTH108 Business Math I Lecture 20.
Matrices and Vector Concepts
Boyce/DiPrima 10th ed, Ch 7.2: Review of Matrices Elementary Differential Equations and Boundary Value Problems, 10th edition, by William E. Boyce and.
Review of Matrix Operations
Chapter 7 Matrix Mathematics
Linear independence and matrix rank
Linear Algebra Lecture 15.
Matrix Operations SpringSemester 2017.
Singular Value Decomposition
Multiplying Matrices.
2. Matrix Algebra 2.1 Matrix Operations.
Lecture 11 Matrices and Linear Algebra with MATLAB
Matrices and Matrix Operations
~ Least Squares example
MATRICES Operations with Matrices Properties of Matrix Operations
Multiplying Matrices.
Matrix Definitions It is assumed you are already familiar with the terms matrix, matrix transpose, vector, row vector, column vector, unit vector, zero.
~ Least Squares example
Matrix Operations SpringSemester 2017.
Multiplying Matrices.
Multiplying Matrices.
Chapter 2 Determinants.
Multiplying Matrices.
Presentation transcript:

By Mary Hudachek-Buswell

Overview

Atmospheric Turbulence Blur

2-D Separable Gaussian Blur This point spread function represents atmospheric turbulence blur.

Toeplitz Matrix Blur Toeplitz matrix structure is a matrix with constants along the descending diagonal. These type matrices are spatially invariant and have zero boundary conditions when combined together.

Illustration of Spatially Invariant BLur Original image Invariant BlurVariant Blur

Illustration of Zero Boundary Conditions Surrounds the object with a black boundary, or zeros on the outside borders of the image

Minimize the 2-Norm of the Residual A is the column blur, B is the row blur, X is the restored image, and C is distorted image. A & B are overdetermined and positive definite. Overdetermined systems have more equations than unknowns and positive definite matrices have a unique quality where given any nonzero vector, z, the product

Least Squares of an Overdetermined System

Least Squares cont.

Necessary Properties The system of recurrence formulas that generates a unique sequence of iterates with such that With the property that at step n, the norm is minimized

Conjugate Gradient Algorithm Initial values for approximation, residual and gradient matrices Scalar to minimize the norm and determine step length Approximate restoration matrix Residual matrix Scalar to measure residual ratio and update gradient Update conjugate gradient search direction

Conjugate Gradient MATLAB C = g; ro = g; p = ro; x = zeros(m, n); for i = 1:maxiter alpha = trace (ro'*ro)/trace(p'*(A*p*B)); x = x + alpha * p; rn = ro - alpha * A * p * B; beta = trace (rn'*rn)/trace(ro'*ro); p = rn + beta * p; ro = rn; End The trace function computes the sum of the diagonal elements of the matrix

Comparison between CG & Inverse Methods

Visual Comparison of Iterations

Concluding Thoughts Direct Methods to find the restoration of a blurred image involve inverting matrices which costs O(n 3 ) Conjugate gradient iterations require transposing matrices, then multiplying by scalars and matrices which costs O(n 2 ), considerably less than direct methods The CG computations are faster and can provide better visual results.