1 citations · 4 across the 6 of their papers we have counts for
9 papers
Text-guided Explorable Image Super-resolution
Kanchana Vaishnavi Gandikota, Paramanand Chandramouli
In this paper, we introduce the problem of zero-shot text-guided exploration of the solutions to open-domain image super-resolution. Our goal is to allow users to explore diverse,…
Evaluating Adversarial Robustness of Low dose CT Recovery
Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Hannah Droege +1
Low dose computed tomography (CT) acquisition using reduced radiation or sparse angle measurements is recommended to decrease the harmful effects of X-ray radiation. Recent works s…
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix
Shashank Agnihotri, Kanchana Vaishnavi Gandikota, Julia Grabinski +2
Following their success in visual recognition tasks, Vision Transformers(ViTs) are being increasingly employed for image restoration. As a few recent works claim that ViTs for imag…
On Adversarial Robustness of Deep Image Deblurring
Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Michael Moeller
Recent approaches employ deep learning-based solutions for the recovery of a sharp image from its blurry observation. This paper introduces adversarial attacks against deep learnin…
Light Field Implicit Representation for Flexible Resolution Reconstruction
Paramanand Chandramouli, Hendrik Sommerhoff, Andreas Kolb
Inspired by the recent advances in implicitly representing signals with trained neural networks, we aim to learn a continuous representation for narrow-baseline 4D light fields. We…
A Generative Model for Generic Light Field Reconstruction
Paramanand Chandramouli, Kanchana Vaishnavi Gandikota, Andreas Goerlitz +2
Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for…