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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

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Computational cameras: convergence of optics and processing.

Changyin Zhou¹, Shree K Nayar

¹Department of Computer Science, Columbia University, New York, NY 10027, USA. changyin@cs.columbia.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|October 25, 2011

Summary

This summary is machine-generated.

Computational cameras use optics and processing to capture images beyond traditional limits. This survey categorizes designs by coding methods and light field representation for enhanced visual information capture.

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Area of Science:

Computer Vision
Optical Engineering
Image Processing

Background:

Computational imaging has rapidly advanced, offering capabilities beyond conventional cameras.
Computational cameras integrate optics and processing to capture richer visual information.

Purpose of the Study:

To provide a comprehensive survey of computational camera designs.
To categorize computational cameras based on their information encoding strategies.
To frame computational cameras as light field projections.

Main Methods:

Taxonomic classification of computational camera designs by coding approaches (object side, pupil plane, sensor side, illumination, arrays, unconventional systems).
Utilizing light field representation as a unifying framework to describe camera designs.
Analyzing optical device transformations of light fields.

Main Results:

A taxonomy categorizing computational cameras based on coding strategies.
Demonstration of light field representation as a tool to understand camera designs.
Illustration of how optical elements encode visual information.

Conclusions:

Computational cameras offer advanced capabilities by encoding more visual information.
Light field representation provides a generalized perspective on computational camera design.
Understanding optical transforms is key to designing effective computational cameras.