Salman Siddique Khan

I am a Postdoctoral Associate at the Rice Computational Imaging Lab. I finished my Ph.D. from the Computational Imaging Lab, IITM, where I worked under Dr. Kaushik Mitra on computational imaging and computer vision. At the Computational Imaging Lab, I worked on developing novel deep learning-based algorithms and designs for lensless imaging systems. As a Ph.D. candidate, I was awarded the Qualcomm Innovation Fellowship for the year 2020-2021 to work on mask-based lensless cameras. Prior to joining the Ph.D. program at IIT Madras, I completed my undergraduate studies in Electronics and Instrumentation Engineering from the National Institute of Technology Rourkela, India.

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I'm primarily interested in computational photography, computer vision and deep learning. Most of my recent works has been focused on using deep learning to develop novel camera designs with form-factor, cost and privacy advantages that can have applications in robotics, healthcare and smart wearables.

We propose a learning-based framework for designing phase-masks for ultra-thin lensless cameras.

We propose a novel physics-inspired neural network for fast and accurate RGB-D estimation from single-shot lensless capture.

We propose an unrolled physics-based network for Fourier Ptychographic reconstructions that outperforms existing methods for low dynamic range sensors.

We propose a general learning based framework to recover photorealistic scenes from lensless captures for both separable and non-separable forward models.

We propose a learning based framework for designing the optical and analog components of a privacy enhancing camera.

We propose a learning based solution to recover photorealistic scenes from globally multiplexed lensless measurements.

Website credits to Jon Barron