• Computer Vision
• Image Processing
• Signal Processing
• Deep Learning

Learning joint temporal embeddings with semantic distillation from multimodal data

Extreme Multi-Label Classification can be effectively solved using Modular approaches

A Divide and Conquer Approach to deal with FTIR based Diabetes Prediction.

A systematic analysis of computer vision applications specific to commercial pig management.

Motion deblurring for IR cameras mounted on a high steering-rate gimbal system (processed in real time).

A Deep Learning approach for motion deblurring for dual-lens cameras to address incoherent views, ill-posedness, and adaptive scale.

Motion deblurring for Rolling shutter cameras, Light field cameras, and Unconstrained Dual-lens cameras.

Motion deblurring for dual-lens cameras possess an ill-posedness, which calls for a prior for depth-consistent deblurring.

Full-resolution light field deblurring can be divided into independent subtasks, wherein a single task reinforces other tasks.

A method for the scenario of a fast moving camera wherein rolling shutter distortions results in intra-frame occlusions.

How can we bridge today's ubiquitous rolling shutter cameras with the well-studied conventional cameras, pertaining to motion deblurring?

Deep Learning is used to obtain the attributes of object motion and camera motion, which is then employed for segmenting moving object(s).

A fully-automatic denoising based on a convex prior on SEM images, which eradicate (difficult to infer or possibly erroneous) noise models.

Wavelet thresholding (Visushrink) is extended to Contourlet transform, in order to enhance the performance of bilateral and NLM filtering.

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