no code implementations • 6 Sep 2020 • Maxime Tremblay, Shirsendu Sukanta Halder, Raoul de Charette, Jean-François Lalonde
In this context, we present a rain rendering pipeline that enables the systematic evaluation of common computer vision algorithms to controlled amounts of rain.
no code implementations • 15 May 2020 • Akella Ravi Tej, Shirsendu Sukanta Halder, Arunav Pratap Shandeelya, Vinod Pankajakshan
In this paper, we show that the root cause of these pattern artifacts can be traced back to a mismatch between the pre-training objective of perceptual loss and the super-resolution objective.
1 code implementation • 10 Apr 2020 • 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 examples.
no code implementations • ICCV 2019 • Shirsendu Sukanta Halder, Jean-François Lalonde, Raoul de Charette
Our rendering relies on a physical particle simulator, an estimation of the scene lighting and an accurate rain photometric modeling to augment images with arbitrary amount of realistic rain or fog.
no code implementations • 27 Nov 2018 • Shirsendu Sukanta Halder, Kanjar De, Partha Pratim Roy
Colours are everywhere.
1 code implementation • 27 Nov 2018 • Sudhanshu Kumar, Shirsendu Sukanta Halder, Kanjar De, Partha Pratim Roy
Traditional approaches in recommendation systems include collaborative filtering and content-based filtering.
no code implementations • 27 Nov 2018 • Shirsendu Sukanta Halder, Sanchayan Santra, Bhabatosh Chanda
In this paper, we propose a Fully Convolutional Neural Network based model to recover the clear scene radiance by estimating the environmental illumination and the scene transmittance jointly from a hazy image.