Search Results for author: Shirsendu Sukanta Halder

Found 7 papers, 2 papers with code

Rain rendering for evaluating and improving robustness to bad weather

no code implementations6 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.

Depth Estimation object-detection +2

Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution

no code implementations15 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.

Image Super-Resolution

MA 3 : Model Agnostic Adversarial Augmentation for Few Shot learning

1 code implementation10 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.

Few-Shot Learning

Physics-Based Rendering for Improving Robustness to Rain

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.

object-detection Object Detection +1

Reconstruction Loss Minimized FCN for Single Image Dehazing

no code implementations27 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.

Image Dehazing Single Image Dehazing

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