Algorithms for Lensless, Miniature Cameras
- Posted By
10Pointer
- Categories
Science & Technology
- Published
14th May, 2021
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Context
Indian Institute of Technology Madras and the Rice University, United States, have developed algorithms for lensless, miniature cameras.
Key facts about the developed algorithms
- Deep Learning was used to develop a reconstruction algorithm called ‘FlatNet’.
- Lensless cameras do not have a lens that acts as a focusing element.
- Due to the absence of the focusing element, the lensless camera captures a multiplexed or globally blurred measurement of the scene.
- Absence of focusing elements restricts their commercial use.
- The developed algorithm is a deep learning algorithm for producing photo-realistic images from the blurred lensless capture.
Algorithm
- It is a finite sequence of well-defined, computer-implementable instructions.
- It is computational solution to the problems.
- It is used to solve a class of problems or to perform a computation.
- Algorithms are always unambiguous and are used as specifications for performing calculations, data processing, automated reasoning, and other tasks.
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Benefits of Lensless camera
- Smart surveillance: There are existing algorithms to deblur images which are based on traditional optimization schemes and yield low-resolution ‘noisy images.’ Lensless captures will be used for endoscopy and smart surveillance.
- Utilization in significant areas: It is used in areas such as Augmented Reality (AR)/ Virtual Reality (VR), security, smart wearables and robotics where cost, form-factor, and weight are major constraints.
