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Related Concept Videos

Spherical Coordinates01:23

Spherical Coordinates

Spherical coordinate systems are preferred over Cartesian, polar, or cylindrical coordinates for systems with spherical symmetry. For example, to describe the surface of a sphere, Cartesian coordinates require all three coordinates. On the other hand, the spherical coordinate system requires only one parameter: the sphere's radius. As a result, the complicated mathematical calculations become simple. Spherical coordinates are used in science and engineering applications like electric and...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
Focusing of Light in the Eye01:16

Focusing of Light in the Eye

Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...

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Related Experiment Video

Updated: May 15, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Real scene capturing using spherical single-element lens camera and improved restoration algorithm for radially

Yupeng Zhang1, Lev G Zimin, Jing Ji

  • 1Graduate School of Information, Production and Systems, Waseda University, Kitakyushu, Fukuoka, Japan. ypng_zhang@aoni.waseda.jp

Optics Express
|December 25, 2012
PubMed
Summary

This study introduces a spherical single-element lens imaging system (SSLIS) for compact cameras. An improved deconvolution algorithm fully restores images captured by SSLIS, reducing artifacts and enhancing quality.

Related Experiment Videos

Last Updated: May 15, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Area of Science:

  • Optics
  • Image Processing
  • Computational Imaging

Background:

  • Compact camera modules require simplified designs and reduced bulk.
  • Spherical single-element lens imaging systems (SSLIS) offer potential for miniaturization and cost reduction.
  • SSLIS inherently produce radially variant blurred images, necessitating advanced deconvolution techniques.

Purpose of the Study:

  • To introduce an improved polar domain deconvolution algorithm for full-field-of-view (FOV) image restoration.
  • To enhance image quality by suppressing artifacts in images captured by SSLIS.
  • To validate the algorithm's effectiveness on both simulated and real-world data.

Main Methods:

  • Development of an improved polar domain deconvolution algorithm.
  • Implementation of boundary interpolation techniques for panoramic polar images.
  • Verification using computer-simulated and real-world images from an SSLIS camera module.

Main Results:

  • Achieved full-FOV image restoration, overcoming limitations of previous partial-FOV methods.
  • Significantly suppressed ringing artifacts in the restored Cartesian images.
  • Demonstrated effective image restoration for radially blurred images from SSLIS.

Conclusions:

  • The improved deconvolution algorithm effectively restores full-FOV images from SSLIS.
  • Boundary interpolation is key to artifact suppression and enhanced image quality.
  • Image restoration quality is influenced by the sparsity of point spread function (PSF) matrices.