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Prabir Kumar Biswas

4 accepted papers

2023

Generative Pipeline for Data Augmentation of Unconstrained Document Images with Structural and Textural Degradation (Student Abstract)

Arnab Poddar, Abhishek Kumar Sah, Soumyadeep Dey, Pratik Jawanpuria, Jayanta Mukhopadhyay, Prabir Kumar Biswas
AAAI 2023technical

Computer vision applications for document image understanding (DIU) such as optical character recognition, word spotting, enhancement etc. suffer from structural deformations like strike-outs and unconstrained strokes, to name a few. They also suffer from texture degradation due to blurring, aging,…

Cited by 0Source ↗PDF ↗Scholar ↗
2021

Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network

Aupendu Kar, Prabir Kumar Biswas
CVPR 2021poster

Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in recent years. However, the network's performance depends on the distribution of the training sets and degrades on out-of-distribution samples. This paper adopts a Bayesian approach for estimating…

Cited by 20PDF ↗code ↗Scholar ↗
2021

Zero-Shot Single Image Restoration Through Controlled Perturbation of Koschmieder's Model

Aupendu Kar, Sobhan Kanti Dhara, Debashis Sen, Prabir Kumar Biswas
CVPR 2021poster

Real-world image degradation due to light scattering can be described based on the Koschmieder's model. Training deep models to restore such degraded images is challenging as real-world paired data is scarcely available and synthetic paired data may suffer from domain-shift issues. In this paper, a…

Cited by 74PDF ↗Scholar ↗
2020

Prior Guided GAN Based Semantic Inpainting

Avisek Lahiri, Arnav Kumar Jain, Sanskar Agrawal, Pabitra Mitra, Prabir Kumar Biswas
CVPR 2020poster

Contemporary deep learning based semantic inpainting can be approached from two directions. First, and the more explored, approach is to train an offline deep regression network over the masked pixels with an additional refinement by adversarial training. This approach requires a single feed-forward…

Cited by 124PDF ↗Scholar ↗