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Related Concept Videos

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Reconstruction of Signal using Interpolation

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Related Experiment Video

Updated: Jul 6, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Signal-processing approaches for image-resolution restoration for TOMBO imagery.

Kerkil Choi1, Timothy J Schulz

  • 1Department of Electrical and Computer Engineering, Michigan Technological University, Houghton, Michigan 49931, USA. kerkil@mtu.edu

Applied Optics
|April 3, 2008
PubMed
Summary
This summary is machine-generated.

A new computational method enhances image resolution by combining diverse, low-resolution measurements from the Thin Observation Module by Bounded Optics (TOMBO) system. This advanced signal processing approach effectively reconstructs high-quality images, even with incomplete or noisy data.

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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Last Updated: Jul 6, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

Area of Science:

  • Optics and Photonics
  • Computational Imaging
  • Signal Processing

Background:

  • Conventional optical systems use large apertures, limiting compactness.
  • The Thin Observation Module by Bounded Optics (TOMBO) utilizes a lenslet array for compact, thin optical systems.
  • TOMBO collects diverse low-resolution measurements, necessitating advanced methods for high-resolution image reconstruction.

Purpose of the Study:

  • To develop and evaluate a computational method for resolution restoration using TOMBO measurements.
  • To address the challenge of efficiently combining diverse low-resolution data into a high-resolution image.
  • To assess the performance of the proposed method through simulations under various noise conditions.

Main Methods:

  • Developed a computational data model based on Fourier optics.
  • Proposed restoration algorithms minimizing Csiszár's I divergence, incorporating Poisson and Gaussian noise models.
  • Adapted the expectation-maximization method for optimization, preserving nonnegativity constraints.
  • Incorporated total variation regularization to mitigate artifacts.

Main Results:

  • Simulations demonstrated the algorithm's ability to produce very high-quality estimates from noiseless data.
  • Reasonably good estimates were achieved from noisy and incomplete measurements.
  • The multiplicative expectation-maximization approach effectively handled nonnegativity constraints.

Conclusions:

  • The developed computational method shows significant promise for high-resolution image reconstruction from TOMBO data.
  • The approach is robust to noise and data incompleteness.
  • Future work could explore measurement selection strategies for further optimization.