Indian Institute of Technology Madras and the Rice University, United States, have developed algorithms for lensless, miniature cameras.
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.
