Computational photography methods and applications

  • How does computational photography work?

    Computational imaging is a set of imaging techniques that combine data acquisition and data processing to create the image of an object through indirect means to yield enhanced resolution, additional information such as optical phase or .

    1. D reconstruction

  • What are the applications of computational imaging?

    Computational Imaging systems cover a broad range of applications include computational microscopy, tomographic imaging, MRI, ultrasound imaging, computational photography, Synthetic Aperture Radar (SAR), seismic imaging etc..

  • What are the computer techniques used in photography?

    Examples: Interpolation, Filtering, Enhancement, Dynamic Range Compression, Color Management, Morphing, Hole Filling, Artistic Image Effects, Image Compression, Watermarking.
    Processing of a set of captured images to create new images..

  • What are the uses of computational photography?

    Computational photography has traditionally been used to make high-quality photographs using hardware that would otherwise not produce such quality, and it's making its way to professional cameras..

  • What is computerized photography?

    Computational photography refers to digital image capture and processing techniques that use digital computation instead of optical processes..

  • Computational photography focuses on leveraging digital computation to capture and process images, while computer vision involves creating digital systems capable of interpreting and analysing visual data, much like the human visual system.
  • Description Computational photography is the convergence of computer graphics, computer vision and imaging.
    Its role is to overcome the limitations of the traditional camera, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world.
Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. Google BooksOriginally published: November 30, 2010

Scholarly articles for computational photography methods and applications

scholar.google.com › citationsComputational photography: methods and applications
LukacCited by 81Computational photography
RaskarCited by 80
Computational photography refers broadly to imaging techniques that enhance or extend the capabilities of digital photography. Google BooksOriginally published: November 30, 2010
Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and a foundation upon which to build solutions for many of today's most interesting and challenging computational imaging problems.
Computational Photography: Methods and Applications provides a strong, fundamental understanding of theory and methods, and a foundation upon which to build 
Computational photography methods and applications
Computational photography methods and applications

Motion blur reduction technology

Coded exposure photography, also known as a flutter shutter, is the name given to any mathematical algorithm that reduces the effects of motion blur in photography.
The key element of the coded exposure process is the mathematical formula that affects the shutter frequency.
This involves the calculation of the relationship between the photon exposure of the light sensor and the randomized code.
The camera is made to take a series of snapshots with random time intervals using a simple computer, this creates a blurred image that can be reconciled into a clear image using the algorithm.

Indirectly forming images from measurements using algorithms

Algorithm

The Kaczmarz method or Kaczmarz's algorithm is an iterative algorithm for solving linear equation systems mwe-math-element>.
It was first discovered by the Polish mathematician Stefan Kaczmarz, and was rediscovered in the field of image reconstruction from projections by Richard Gordon, Robert Bender, and Gabor Herman in 1970, where it is called the Algebraic Reconstruction Technique (ART).
ART includes the positivity constraint, making it nonlinear.
Simultaneous localization and mapping (SLAM) is the computational problem of

Simultaneous localization and mapping (SLAM) is the computational problem of

Computational navigational technique used by robots and autonomous vehicles

Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.
While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments.
Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM.
SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality.

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