DOCSLIB.ORG

Deconvolution with ADMM

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Deconvolution with ADMM Deconvolution with ADMM EE367/CS448I: Computational Imaging and Display stanford.edu/class/ee367 Lecture 6 Gordon Wetzstein Stanford University Lens as Optical Low-pass Filter • point source on focal plane maps to point focal plane Lens as Optical Low-pass Filter • away from focal plane: out of focus blur blurred point focal plane Lens as Optical Low-pass Filter • shift-invariant convolution focal plane Lens as Optical Low-pass Filter convolution kernel is called point spread function (PSF) c b = c∗ x x b sharp image measured, blurred image point spread function point (PSF): Lens as Optical Low-pass Filter diffraction-limited PSF of circular aperture (aka “Airy” pattern): c b = c∗ x x b sharp image measured, blurred image point spread function point (PSF): PSF, OTF, MTF • point spread function (PSF) is fundamental concept in optics • optical transfer function (OTF) is (complex) Fourier transform of PSF • modulation transfer function (MTF) is magnitude of OTF • example: MTF=|OTF| OTF=F{PSF} PSF PSF, OTF, MTF • example: MTF=|OTF| OTF=F{PSF} PSF Deconvolution • given measurements b and convolution kernel c, what is x? c x b * ? = Deconvolution with Inverse Filtering −1 −1 ⎧F{b}⎫ • naive solution: apply inverse kernel x! = c ∗b = F ⎨ ⎬ F c ⎩ { } ⎭ x x! Deconvolution with Inverse Filtering & Noise −1 −1 ⎧F{b}⎫ • naive solution: apply inverse kernel x! = c ∗b = F ⎨ ⎬ F c ⎩ { } ⎭ x! • Gaussian noise, σ = 0.05 Deconvolution with Inverse Filtering & Noise • results: terrible! • why? this is an ill-posed problem (division by (close to) zero in frequency domain) à noise is drastically amplified! • need to include prior(s) on images to make up for lost data • for example: noise statistics (signal to noise ratio) Deconvolution with Wiener Filtering • apply inverse kernel and don’t divide by 0 ⎧ 2 ⎫ −1 ⎪ F{c} F{b}⎪ x! = F ⋅ ⎨ 2 1 ⎬ ⎪ F{c} + F{c} ⎪ ⎩ SNR ⎭ amplitude-dependent damping factor! ���� ������ ≈ 0.5 ��� = ����� ��� = � Deconvolution with Wiener Filtering Naïve inverse filter Wiener x x! Deconvolution with Wiener Filtering σ = 0.01 σ = 0.05 σ = 0.1 Deconvolution with Wiener Filtering • results: not too bad, but noisy • this is a heuristic à dampen noise amplification Total Variation 2 2 minimize Cx − b + λTV(x) = minimize Cx − b + λ ∇x x 2 x 2 1 x = x 1 ∑ i i • idea: promote sparse gradients (edges) • ∇ is finite differences operator, i.e. matrix ⎡ −1 1 ⎤ ⎢ 1 1 ⎥ ⎢ − ⎥ ⎢ ! ⎥ ⎣⎢ −1 ⎦⎥ Rudin et al. 1992 Total Variation express (forward finite difference) ⎡ 0 0 0 ⎤ ⎡ 0 0 0 ⎤ ⎢ 0 1 1 ⎥ ∗⎢ 0 1 0 ⎥ gradient as convolution! ∗⎢ − ⎥ ⎢ − ⎥ ⎣⎢ 0 0 0 ⎦⎥ ⎣⎢ 0 1 0 ⎦⎥ x ∇x x ∇y x Total Variation better: isotropic easier: anisotropic 2 2 2 2 x (∇x x) + (∇y x) (∇x x) + (∇y x) Total Variation • for simplicity, this lecture only discusses anisotropic TV: ⎡ ⎤ ∇x TV(x) = ∇ x + ∇ x = ⎢ ⎥ x x 1 y 1 ⎢ ∇y ⎥ ⎣ ⎦ 1 • problem: l1-norm doesn’t allow for an inverse filtering approach • however: simple solution for data fitting along and simple solution for TV alone à split problem! Deconvolution with ADMM • split deconvolution with TV prior: 2 minimize Cx − b 2 + λ z 1 subject to ∇x = z • general form of ADMM (alternating direction method of multiplies): 2 f (x) = Cx − b minimize f (x) + g(z) 2 g(z) = λ z subject to Ax + Bz = c 1 A = ∇, B = −I, c = 0 Deconvolution with ADMM • split deconvolution with TV prior: 2 minimize Cx − b 2 + λ z 1 subject to ∇x = z • general form of ADMM (alternating direction method of multiplies): 2 f (x) = Cx − b minimize f (x) + g(z) 2 g(z) = λ z subject to Ax + Bz = c 1 A = ∇, B = −I, c = 0 Deconvolution with ADMM • split deconvolution with TV prior: 2 minimize Cx − b 2 + λ z 1 subject to ∇x = z • general form of ADMM (alternating direction method of multiplies): 2 f (x) = Cx − b minimize f (x) + g(z) 2 g(z) = λ z subject to Ax + Bz = c 1 A = ∇, B = −I, c = 0 Deconvolution with ADMM • split deconvolution with TV prior: 2 minimize Cx − b 2 + λ z 1 subject to ∇x = z • general form of ADMM (alternating direction method of multiplies): 2 f (x) = Cx − b minimize f (x) + g(z) 2 g(z) = λ z subject to Ax + Bz = c 1 A = ∇, B = −I, c = 0 minimize f (x) + g(z) ADMM subject to Ax + Bz = c • Lagrangian (bring constraints into objective = penalty method): L(x, y,z) = f (x) + g(z) + yT (Ax + Bz − c) dual variable or Lagrange multiplier • augmented Lagrangian: additional penalty term T 2 Lρ (x, y,z) = f (x) + g(z) + y (Ax + Bz − c) + (ρ / 2) Ax + Bz − c 2 minimize f (x) + g(z) ADMM subject to Ax + Bz = c • augmented Lagrangian is differentiable under mild conditions (usually better convergence etc.) T 2 Lρ (x, y,z) = f (x) + g(z) + y (Ax + Bz − c) + (ρ / 2) Ax + Bz − c 2 minimize f (x) + g(z) ADMM subject to Ax + Bz = c • ADMM consists of 3 steps per iteration k: k+1 k k x := argmin Lρ (x,z , y ) x k+1 k+1 k z := argmin Lρ (x ,z, y ) z yk+1 := yk + ρ(Axk+1 + Bzk+1 − c) minimize f (x) + g(z) ADMM subject to Ax + Bz = c • ADMM consists of 3 steps per iteration k: constant xk+1 := argmin f (x) + (ρ / 2) Ax + Bzk − c + u k x ( ) zk+1 := argmin g(z) + (ρ / 2) Axk+1 + Bz − c + u k z ( ) u k+1 := u k + Axk+1 + Bzk+1 − c scaled dual variable: u = (1/ ρ)y minimize f (x) + g(z) ADMM subject to Ax + Bz = c • ADMM consists of 3 steps per iteration k: split f(x) and g(x) into independent problems! (u connects them) 2 xk+1 := argmin f (x) + (ρ / 2) Ax + Bzk − c + u k 2 x ( ) 2 zk+1 := argmin g(z) + (ρ / 2) Axk+1 + Bz − c + u k 2 z ( ) u k+1 := u k + Axk+1 + Bzk+1 − c scaled dual variable: u = (1/ ρ)y 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 • ADMM consists of 3 steps per iteration k: k+1 ⎛ 1 2 k k 2 ⎞ x := argmin Cx − b + (ρ / 2) ∇x − z + u ⎜ 2 2 ⎟ x ⎝ 2 ⎠ 2 zk+1 := argmin λ z + (ρ / 2) ∇xk+1 − z + u k 1 2 z ( ) u k+1 := u k + ∇xk+1 − zk+1 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 constant, say v = zk − u k k+1 ⎛ 1 2 k k 2 ⎞ 1. x-update: x := argmin Cx − b + (ρ / 2) ∇x − z + u ⎜ 2 2 ⎟ x ⎝ 2 ⎠ solve normal equations (CT C + ρ∇T ∇)x = (CT b + ρ∇T v) T ⎡ ⎤ ∇x ∇T v = ⎢ ⎥ v = ∇T v + ∇T v ∇ x 1 y 2 ⎣⎢ y ⎦⎥ 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 constant, say v = zk − u k k+1 ⎛ 1 2 k k 2 ⎞ 1. x-update: x := argmin Cx − b + (ρ / 2) ∇x − z + u ⎜ 2 2 ⎟ x ⎝ 2 ⎠ −1 x = (CT C + ρ∇T ∇) (CT b + ρ∇T v) * * ⎧ F c * F b F F v F F v ⎫ ⎪ { } ⋅ { } + ρ {∇x } ⋅ { 1} + {∇y } ⋅ { 2 } ⎪ • inverse filtering: xk+1 = F −1 ( ) ⎨ * * * ⎬ ⎪F{c} ⋅ F{c} + ρ F{∇x } ⋅ F{∇x } + F ∇y ⋅ F ∇y ⎪ ⎩ ( { } { })⎭ precompute! à may blow up, but that’s okay 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 constant, say a = ∇xk+1 + u k 2 2. z-update: zk+1 := argmin λ z + (ρ / 2) ∇xk+1 − z + u k 1 2 z ( ) 2 := argminλ z 1 + (ρ / 2) z − a 2 z • l1-norm is not differentiable! yet, closed-form solution via element-wise soft thresholding: ⎧ a −κ a >κ k+1 ⎪ z := Sλ/ρ (a) Sκ (a) = ⎨ 0 a ≤κ = (a −κ )+ − (−a −κ )+ ⎪ κ = λ / ρ ⎩ a +κ a < −κ 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 for k=1:max_iters ⎛ 2 ⎞ k 1 1 ⎡ C ⎤ ⎡ b ⎤ x + := argmin ⎜ ⎢ ⎥ x − ⎢ ⎥ ⎟ inverse filtering x ⎜ 2 ρ∇ ρv ⎟ ⎝ ⎣⎢ ⎦⎥ ⎣⎢ ⎦⎥ 2 ⎠ k+1 k+1 k element-wise threshold z := Sλ/ρ (∇x + u ) u k+1 := u k + ∇xk+1 − zk+1 trivial 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 for k=1:max_iters ⎛ 2 ⎞ k 1 1 ⎡ C ⎤ ⎡ b ⎤ x + := argmin ⎜ ⎢ ⎥ x − ⎢ ⎥ ⎟ inverse filtering x ⎜ 2 ρ∇ ρv ⎟ ⎝ ⎣⎢ ⎦⎥ ⎣⎢ ⎦⎥ 2 ⎠ k+1 k+1 k element-wise threshold z := Sλ/ρ (∇x + u ) u k+1 := u k + ∇xk+1 − zk+1 trivial à easy! J 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 Wiener filtering ADMM with anisotropic TV, λ = 0.01, ρ = 10 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 • too much TV: “patchy”, too little TV: noisy λ = 0.01, ρ = 10 λ = 0.05, ρ = 10 λ = 0.1, ρ = 10 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 Wiener filtering ADMM with anisotropic TV, λ = 0.1, ρ = 10 1 2 minimize Cx − b + λ z Deconvolution with ADMM 2 2 1 subject to ∇x − z = 0 • too much TV: okay because image actually has sparse gradients! λ = 0.01, ρ = 10 λ = 0.05, ρ = 10 λ = 0.1, ρ = 10 Outlook ADMM • powerful tool for many computational imaging problems • include generic prior in g(z), just need to derive proximal operator 1 2 minimize f (x) + g(z) minimize Ax − b + Γ(x) {x,z} x 2 2 % !#"#$ regularization subject to Ax = z data fidelity • example priors: noise statistics, sparse gradient, smoothness, … • weighted sum of different priors also possible • anisotropic TV is one of the easiest priors Remember! • implement matrix-free operations for Ax and A’x if efficient (e.g.
Load more

Recommended publications
  • Convolution! (CDT-14) Luciano Da Fontoura Costa
    Convolution! (CDT-14) Luciano da Fontoura Costa To cite this version: Luciano da Fontoura Costa. Convolution! (CDT-14). 2019. hal-02334910 HAL Id: hal-02334910 https://hal.archives-ouvertes.fr/hal-02334910 Preprint submitted on 27 Oct 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Convolution! (CDT-14) Luciano da Fontoura Costa [email protected] S~aoCarlos Institute of Physics { DFCM/USP October 22, 2019 Abstract The convolution between two functions, yielding a third function, is a particularly important concept in several areas including physics, engineering, statistics, and mathematics, to name but a few. Yet, it is not often so easy to be conceptually understood, as a consequence of its seemingly intricate definition. In this text, we develop a conceptual framework aimed at hopefully providing a more complete and integrated conceptual understanding of this important operation. In particular, we adopt an alternative graphical interpretation in the time domain, allowing the shift implied in the convolution to proceed over free variable instead of the additive inverse of this variable. In addition, we discuss two possible conceptual interpretations of the convolution of two functions as: (i) the `blending' of these functions, and (ii) as a quantification of `matching' between those functions.
    [Show full text]
  • Fourier Ptychographic Wigner Distribution Deconvolution
    FOURIER PTYCHOGRAPHIC WIGNER DISTRIBUTION DECONVOLUTION Justin Lee1, George Barbastathis2,3 1. Department of Health Sciences and Technology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA 2. Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA 3. Singapore–MIT Alliance for Research and Technology (SMART) Centre Singapore 138602, Singapore Email: [email protected] KEY WORDS: Computational Imaging, phase retrieval, ptychography, Fourier ptychography Ptychography was first proposed as a method for phase retrieval by Hoppe in the late 1960s [1]. A typical ptychographic setup involves generation of illumination diversity via lateral translation of an object relative to its illumination (often called the probe) and measurement of the intensity of the far-field diffraction pattern/Fourier transform of the illuminated object. In the decades following Hoppes initial work, several important extensions to ptychography were developed, including an ability to simultaneously retrieve the amplitude and phase of both the probe and the object via application of an extended ptychographic iterative engine (ePIE) [2] and a non-iterative technique for phase retrieval using ptychographic information called Wigner Distribution Deconvolution (WDD) [3,4]. Most recently, the Fourier dual to ptychography was introduced by the authors in [5] as an experimentally simple technique for phase retrieval using an LED array as the illumination source in a standard microscope setup. In Fourier ptychography, lateral translation of the object relative to the probe occurs in frequency space and is obtained by tilting the angle of illumination (while holding the pupil function of the imaging system constant). In the past few years, several improvements to Fourier ptychography have been introduced, including an ePIE-like technique for simultaneous pupil function and object retrieval [6].
    [Show full text]
  • Enhanced Three-Dimensional Deconvolution Microscopy Using a Measured Depth-Varying Point-Spread Function
    2622 J. Opt. Soc. Am. A/Vol. 24, No. 9/September 2007 J. W. Shaevitz and D. A. Fletcher Enhanced three-dimensional deconvolution microscopy using a measured depth-varying point-spread function Joshua W. Shaevitz1,3,* and Daniel A. Fletcher2,4 1Department of Integrative Biology, University of California, Berkeley, California 94720, USA 2Department of Bioengineering, University of California, Berkeley, California 94720, USA 3Department of Physics, Princeton University, Princeton, New Jersey 08544, USA 4E-mail: fl[email protected] *Corresponding author: [email protected] Received February 26, 2007; revised April 23, 2007; accepted May 1, 2007; posted May 3, 2007 (Doc. ID 80413); published July 27, 2007 We present a technique to systematically measure the change in the blurring function of an optical microscope with distance between the source and the coverglass (the depth) and demonstrate its utility in three- dimensional (3D) deconvolution. By controlling the axial positions of the microscope stage and an optically trapped bead independently, we can record the 3D blurring function at different depths. We find that the peak intensity collected from a single bead decreases with depth and that the width of the axial, but not the lateral, profile increases with depth. We present simple convolution and deconvolution algorithms that use the full depth-varying point-spread functions and use these to demonstrate a reduction of elongation artifacts in a re- constructed image of a 2 ␮m sphere. © 2007 Optical Society of America OCIS codes: 100.3020, 100.6890, 110.0180, 140.7010, 170.6900, 180.2520. 1. INTRODUCTION mismatch between the objective lens and coupling oil can Three-dimensional (3D) deconvolution microscopy is a be severe [5,6], especially in systems using high- powerful tool for visualizing complex biological struc- numerical apertures.
    [Show full text]
  • Sequences Systems Convolution Dirac's Delta Function Fourier
    Sequences Dirac’s Delta Function ( 1 0 x 6= 0 fxngn=−∞ is a discrete sequence. δ(x) = , R 1 δ(x)dx = 1 1 x = 0 −∞ Can derive a sequence from a function x(t) by having xn = n R 1 x(tsn) = x( ). Sampling: f(x)δ(x − a)dx = f(a) fs −∞ P1 P1 Absolute summability: n=−∞ jxnj < 1 Dirac comb: s(t) = ts n=−∞ δ(t − tsn), x^(t) = x(t)s(t) P1 2 Square summability: n=−∞ jxnj < 1 (aka energy signal) Periodic: 9k > 0 : 8n 2 Z : xn = xn+k Fourier Transform ( 0 n < 0 j!n un = is the unit-step sequence Discrete Sejong’s of the form xn = e are eigensequences with 1 n ≥ 0 respect to a system T because for any !, there is some H(!) ( such that T fxng = H(!)fxng 1 n = 0 1 δn = is the impulse sequence Ffg(t)g(!) = G(!) = R g(t)e∓j!tdt 0 n 6= 0 −∞ −1 R 1 ±j!t F fG(!)g(t) = g(t) = −∞ G(!)e dt Systems Linearity: ax(t) + by(t) aX(f) + bY (f) 1 f Time scaling: x(at) jaj X( a ) 1 t A discrete system T transforms a sequence: fyng = T fxng. Frequency scaling: jaj x( a ) X(af) −2πjf∆t Causal systems cannot look into the future: yn = Time shifting: x(t − ∆t) X(f)e f(xn; xn−1; xn−2;:::) 2πj∆ft Frequency shifting: x(t)e X(f − ∆f) Memoryless systems depend only on the current input: yn = R 1 2 R 1 2 Parseval’s theorem: 1 jx(t)j dt = −∞ jX(f)j df f(xn) Continuous convolution: Ff(f ∗ g)(t)g = Fff(t)gFfg(t)g Delay systems shift sequences in time: yn = xn−d Discrete convolution: fxng ∗ fyng = fzng () A system T is time invariant if for all d: fyng = T fxng () X(ej!)Y (ej!) = Z(ej!) fyn−dg = T fxn−dg 0 A system T is linear if for any sequences: T faxn + bxng = 0 Sample Transforms aT fxng + bT fxng Causal linear time-invariant systems are of the form The Fourier transform preserves function even/oddness but flips PN PM real/imaginaryness iff x(t) is odd.
    [Show full text]
  • The Role of the Wigner Distribution Function in Iterative Ptychography T B Edo1
    The role of the Wigner distribution function in iterative ptychography T B Edo1 1 Department of Physics and Astronomy, University of Sheffield, S3 7RH, United Kingdom E-mail: [email protected] Ptychography employs a set of diffraction patterns that capture redundant information about an illuminated specimen as a localized beam is moved over the specimen. The robustness of this method comes from the redundancy of the dataset that in turn depends on the amount of oversampling and the form of the illumination. Although the role of oversampling in ptychography is fairly well understood, the same cannot be said of the illumination structure. This paper provides a vector space model of ptychography that accounts for the illumination structure in a way that highlights the role of the Wigner distribution function in iterative ptychography. Keywords: ptychography; iterative phase retrieval; coherent diffractive imaging. 1. Introduction Iterative phase retrieval (IPR) algorithms provide a very efficient way to recover the specimen function from a set of diffraction patterns in ptychography. The on-going developments behind the execution of these algorithms are pushing the boundaries of the method by extracting more information from the diffraction patterns than was earlier thought possible when iterative ptychography was first proposed [1]. Amongst these are: simultaneous recovery of the illumination (or probe) alongside the specimen [2, 3, 4], super resolved specimen recovery [5, 6], probe position correction [2, 7, 8, 9], three-dimensional specimen recovery [10, 11, 23], ptychography using under sampled diffraction patterns [12, 13, 14] and the use of partial coherent source in diffractive imaging experiments [15, 16, 17, 18].
    [Show full text]
  • Kernel Deconvolution Density Estimation Guillermo Basulto-Elias Iowa State University
    Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2016 Kernel deconvolution density estimation Guillermo Basulto-Elias Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Statistics and Probability Commons Recommended Citation Basulto-Elias, Guillermo, "Kernel deconvolution density estimation" (2016). Graduate Theses and Dissertations. 15874. https://lib.dr.iastate.edu/etd/15874 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Kernel deconvolution density estimation by Guillermo Basulto-Elias A dissertation submitted to the graduate faculty in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Major: Statistics Program of Study Committee: Alicia L. Carriquiry, Co-major Professor Kris De Brabanter, Co-major Professor Daniel J. Nordman, Co-major Professor Kenneth J. Koehler Yehua Li Lily Wang Iowa State University Ames, Iowa 2016 Copyright c Guillermo Basulto-Elias, 2016. All rights reserved. ii DEDICATION To Mart´ın.I could not have done without your love and support. iii TABLE OF CONTENTS LIST OF TABLES . vi LIST OF FIGURES . vii ACKNOWLEDGEMENTS . xi CHAPTER 1. INTRODUCTION . 1 CHAPTER 2. A NUMERICAL INVESTIGATION OF BIVARIATE KER- NEL DECONVOLUTION WITH PANEL DATA . 4 Abstract . .4 2.1 Introduction . .4 2.2 Problem description . .7 2.2.1 A simplified estimation framework . .7 2.2.2 Kernel deconvolution estimation with panel data .
    [Show full text]
  • Wavelet Deconvolution in a Periodic Setting
    J. R. Statist. Soc. B (2004) 66, Part 3, pp. 547–573 Wavelet deconvolution in a periodic setting Iain M. Johnstone, Stanford University, USA G´erard Kerkyacharian, Centre National de la Recherche Scientifique and Universit´e de Paris X, France Dominique Picard Centre National de la Recherche Scientifique and Universit´e de Paris VII, France and Marc Raimondo University of Sydney, Australia [Read before The Royal Statistical Society at a meeting organized by the Research Section on ‘Statistical approaches to inverse problems’ on Wednesday, December 10th, 2003, Professor J. T. Kent in the Chair ] Summary. Deconvolution problems are naturally represented in the Fourier domain, whereas thresholding in wavelet bases is known to have broad adaptivity properties. We study a method which combines both fast Fourier and fast wavelet transforms and can recover a blurred function observed in white noise with O{n log.n/2} steps. In the periodic setting, the method applies to most deconvolution problems, including certain ‘boxcar’ kernels, which are important as a model of motion blur, but having poor Fourier characteristics. Asymptotic theory informs the choice of tuning parameters and yields adaptivity properties for the method over a wide class of measures of error and classes of function. The method is tested on simulated light detection and ranging data suggested by underwater remote sensing. Both visual and numerical results show an improvement over competing approaches. Finally, the theory behind our estimation paradigm gives a complete characterization of the ‘maxiset’ of the method: the set of functions where the method attains a near optimal rate of convergence for a variety of Lp loss functions.
    [Show full text]
  • This Is a Repository Copy of Ptychography. White Rose
    This is a repository copy of Ptychography. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/127795/ Version: Accepted Version Book Section: Rodenburg, J.M. orcid.org/0000-0002-1059-8179 and Maiden, A.M. (2019) Ptychography. In: Hawkes, P.W. and Spence, J.C.H., (eds.) Springer Handbook of Microscopy. Springer Handbooks . Springer . ISBN 9783030000684 https://doi.org/10.1007/978-3-030-00069-1_17 This is a post-peer-review, pre-copyedit version of a chapter published in Hawkes P.W., Spence J.C.H. (eds) Springer Handbook of Microscopy. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-00069-1_17. Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Ptychography John Rodenburg and Andy Maiden Abstract: Ptychography is a computational imaging technique. A detector records an extensive data set consisting of many inference patterns obtained as an object is displaced to various positions relative to an illumination field.
    [Show full text]
  • Deconvolution of the Fourier Spectrum
    Koninklijk Meteorologisch Instituut van Belgi¨e Institut Royal M´et´eorologique de Belgique Deconvolution of the Fourier spectrum F. De Meyer 2003 Wetenschappelijke en Publication scientifique technische publicatie et technique Nr 33 No 33 Uitgegeven door het Edit´epar KONINKLIJK METEOROLOGISCH l’INSTITUT ROYAL INSTITUUT VAN BELGIE METEOROLOGIQUE DE BELGIQUE Ringlaan 3, B -1180 Brussel Avenue Circulaire 3, B -1180 Bruxelles Verantwoordelijke uitgever: Dr. H. Malcorps Editeur responsable: Dr. H. Malcorps Koninklijk Meteorologisch Instituut van Belgi¨e Institut Royal M´et´eorologique de Belgique Deconvolution of the Fourier spectrum F. De Meyer 2003 Wetenschappelijke en Publication scientifique technische publicatie et technique Nr 33 No 33 Uitgegeven door het Edit´epar KONINKLIJK METEOROLOGISCH l’INSTITUT ROYAL INSTITUUT VAN BELGIE METEOROLOGIQUE DE BELGIQUE Ringlaan 3, B -1180 Brussel Avenue Circulaire 3, B -1180 Bruxelles Verantwoordelijke uitgever: Dr. H. Malcorps Editeur responsable: Dr. H. Malcorps 1 Abstract The problem of estimating the frequency spectrum of a real continuous function, which is measured only at a finite number of discrete times, is discussed in this tutorial review. Based on the deconvolu- tion method, the complex version of the one-dimensional CLEAN algorithm provides a simple way to remove the artefacts introduced by the sampling and the effects of missing data from the computed Fourier spectrum. The technique is very appropriate in the case of equally spaced data, as well as for data samples randomly distributed in time. An example of the application of CLEAN to a synthetic spectrum of a small number of harmonic components at discrete frequencies is shown. The case of a low signal-to-noise time sequence is also presented by illustrating how CLEAN recovers the spectrum of the declination component of the geomagnetic field, measured in the magnetic observatory of Dourbes.
    [Show full text]
  • Locating Events Using Time Reversal and Deconvolution: Experimental Application and Analysis
    J Nondestruct Eval (2015) 34:2 DOI 10.1007/s10921-015-0276-x Locating Events Using Time Reversal and Deconvolution: Experimental Application and Analysis Johannes Douma · Ernst Niederleithinger · Roel Snieder Received: 28 March 2014 / Accepted: 13 January 2015 © Springer Science+Business Media New York 2015 Abstract Time reversal techniques are used in ocean 1 Introduction acoustics, medical imaging, seismology, and non-destructive evaluation to backpropagate recorded signals to the source Several methods are used to evaluate acoustic signals gen- of origin. We demonstrate experimentally a technique which erated by events in media such as water, rocks, metals or improves the temporal focus achieved at the source location concrete. Most of them are summarized as acoustic emis- by utilizing deconvolution. One experiment consists of prop- sion methods (AE), which are used to localize and character- agating a signal from a transducer within a concrete block ize the point of origin of the events. Sophisticated methods to a single receiver on the surface, and then applying time have been developed in seismology to localize and charac- reversal or deconvolution to focus the energy back at the terize earthquakes. Time reversal (TR) has been a topic of source location. Another two experiments are run to study the much research in acoustics due to its robust nature and abil- robust nature of deconvolution by investigating the effect of ity to compress the measured scattered waveforms back at changing the stabilization constant used in the deconvolution the source point in both space and time [1–4]. This has led to and the impact multiple sources have upon deconvolutions’ TR being applied in a wide variety of fields such as medicine, focusing abilities.
    [Show full text]
  • 13.1 Convolution and Deconvolution Using the FFT 531
    13.1 Convolution and Deconvolution Using the FFT 531 13.1 Convolution and Deconvolution Using the FFT We have defined the convolution of two functions for the continuous case in equation (12.0.8), and have given the convolution theorem as equation (12.0.9). The http://www.nr.com or call 1-800-872-7423 (North America only), or send email to [email protected] (outside North Amer readable files (including this one) to any server computer, is strictly prohibited. To order Numerical Recipes books or CDROMs, v Permission is granted for internet users to make one paper copy their own personal use. Further reproduction, or any copyin Copyright (C) 1986-1992 by Cambridge University Press. Programs Copyright (C) 1986-1992 by Numerical Recipes Software. Sample page from NUMERICAL RECIPES IN FORTRAN 77: THE ART OF SCIENTIFIC COMPUTING (ISBN 0-521-43064-X) theorem says that the Fourier transform of the convolution of two functions is equal to the product of their individual Fourier transforms. Now, we want to deal with the discrete case. We will mention first the context in which convolution is a useful procedure, and then discuss how to compute it efficiently using the FFT. The convolution of two functions r(t) and s(t), denoted r ∗ s, is mathematically equal to their convolution in the opposite order, s ∗ r. Nevertheless, in most applications the two functions have quite different meanings and characters. One of the functions, say s, is typically a signal or data stream, which goes on indefinitely in time (or in whatever the appropriate independent variable may be).
    [Show full text]
  • Deconvolutional Time Series Regression: a Technique for Modeling Temporally Diffuse Effects
    Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects Cory Shain William Schuler Department of Linguistics Department of Linguistics The Ohio State University The Ohio State University [email protected] [email protected] Abstract dard approach for handling the possibility of tem- porally diffuse relationships between the predic- Researchers in computational psycholinguis- tors and the response is to use spillover or lag tics frequently use linear models to study regressors, where the observed predictor value is time series data generated by human subjects. used to predict subsequent observations of the re- However, time series may violate the assump- tions of these models through temporal diffu- sponse (Erlich and Rayner, 1983). But this strat- sion, where stimulus presentation has a linger- egy has several undesirable properties. First, the ing influence on the response as the rest of choice of spillover position(s) for a given predic- the experiment unfolds. This paper proposes a tor is difficult to motivate empirically. Second, in new statistical model that borrows from digital experiments with variably long trials the use of signal processing by recasting the predictors relative event indices obscures potentially impor- and response as convolutionally-related sig- tant details about the actual amount of time that nals, using recent advances in machine learn- ing to fit latent impulse response functions passed between events. And third, including mul- (IRFs) of arbitrary shape. A synthetic exper- tiple spillover positions per predictor quickly leads iment shows successful recovery of true la- to parametric explosion on realistically complex tent IRFs, and psycholinguistic experiments models over realistically sized data sets, especially reveal plausible, replicable, and fine-grained if random effects structures are included.
    [Show full text]