119 citations · 119 across the 3 of their papers we have counts for
6 papers
Rain rendering for evaluating and improving robustness to bad weather
Maxime Tremblay, Shirsendu Sukanta Halder, Raoul de Charette +1
Rain fills the atmosphere with water particles, which breaks the common assumption that light travels unaltered from the scene to the camera. While it is well-known that rain affec…
Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
Akella Ravi Tej, Shirsendu Sukanta Halder, Arunav Pratap Shandeelya +1
Single image super-resolution (SISR) is an ill-posed problem with an indeterminate number of valid solutions. Solving this problem with neural networks would require access to exte…
MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning
Rohit Jena, Shirsendu Sukanta Halder, Katia Sycara
Despite the recent developments in vision-related problems using deep neural networks, there still remains a wide scope in the improvement of generalizing these models to unseen ex…
Physics-Based Rendering for Improving Robustness to Rain
Shirsendu Sukanta Halder, Jean-François Lalonde, Raoul de Charette
To improve the robustness to rain, we present a physically-based rain rendering pipeline for realistically inserting rain into clear weather images. Our rendering relies on a physi…
Perceptual Conditional Generative Adversarial Networks for End-to-End Image Colourization
Shirsendu Sukanta Halder, Kanjar De, Partha Pratim Roy
Colours are everywhere. They embody a significant part of human visual perception. In this paper, we explore the paradigm of hallucinating colours from a given gray-scale image. Th…
Reconstruction Loss Minimized FCN for Single Image Dehazing
Shirsendu Sukanta Halder, Sanchayan Santra, Bhabatosh Chanda
Haze and fog reduce the visibility of outdoor scenes as a veil like semi-transparent layer appears over the objects. As a result, images captured under such conditions lack contras…