1 citations · 1 across the 4 of their papers we have counts for
5 papers
BurstGP: Enhancing Raw Burst Image Super Resolution with Generative Priors
Dong Huo, Tristan Aumentado-Armstrong, Samrudhdhi B. Rangrej +8
Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) frames, relying on temporal r…
Augmenting Perceptual Super-Resolution via Image Quality Predictors
Fengjia Zhang, Samrudhdhi B. Rangrej, Tristan Aumentado-Armstrong +2
Super-resolution (SR), a classical inverse problem in computer vision, is inherently ill-posed, inducing a distribution of plausible solutions for every input. However, the desired…
Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes
Samrudhdhi B. Rangrej, Chetan L. Srinidhi, James J. Clark
Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses. However, in many appli…
Visual Attention in Imaginative Agents
Samrudhdhi B. Rangrej, James J. Clark
We present a recurrent agent who perceives surroundings through a series of discrete fixations. At each timestep, the agent imagines a variety of plausible scenes consistent with t…
A deep learning framework for segmentation of retinal layers from OCT images
Karthik Gopinath, Samrudhdhi B Rangrej, Jayanthi Sivaswamy
Segmentation of retinal layers from Optical Coherence Tomography (OCT) volumes is a fundamental problem for any computer aided diagnostic algorithm development. This requires prepr…