Indian Institute of Technology (IIT), Madras along with Rice University, US have developed an algorithm for lensless, miniature cameras supported with augmented reality or virtual reality, security, smart wearables and robotics where cost, form-factor and weight are major constraints.
Lensless cameras do not have a lens which, in a conventional camera, acts as a focusing element allowing the sensor to capture a sharp photograph of the scene. Due to the absence of this focusing element, the lensless camera captures a multiplexed or globally blurred measurement of the scene.
The researchers have developed a deep learning algorithm for producing photo-realistic images from the blurred lensless capture.
Dr. Kaushik Mitra, Head of Computational Imaging Laboratory, IIT Madras and Assistant Professor, Department of Electrical Engineering, said, “Existing algorithms to deblur images based on traditional optimization schemes yield low-resolution ‘noisy images’.
He further said that, “Our research team used ‘Deep Learning’ to develop a reconstruction algorithm called ‘FlatNet’ for lensless cameras which resulted in significant improvement over traditional optimization-based algorithms. FlatNet was tested on various real and challenging scenarios and was found to be effective in de-blurring images captured by the lensless camera”.
The research has been funded by National Science Foundation and NSF EXPEDITIONS, US, Neural Engineering System Design (NESD), Defense Advanced Research Projects Agency (DARPA), US, National Institutes of Health (NIH) Grant, US and Qualcomm Innovation Fellowship India 2020.