94 citations · 343 across the 25 of their papers we have counts for
27 papers
Light-Field Dataset for Disparity Based Depth Estimation
Suresh Nehra, Aupendu Kar, Jayanta Mukhopadhyay +1
A Light Field (LF) camera consists of an additional two-dimensional array of micro-lenses placed between the main lens and sensor, compared to a conventional camera. The sensor pix…
Sharing the Learned Knowledge-base to Estimate Convolutional Filter Parameters for Continual Image Restoration
Aupendu Kar, Krishnendu Ghosh, Prabir Kumar Biswas
Continual learning is an emerging topic in the field of deep learning, where a model is expected to learn continuously for new upcoming tasks without forgetting previous experience…
Artificially Generated Visual Scanpath Improves Multi-label Thoracic Disease Classification in Chest X-Ray Images
Ashish Verma, Aupendu Kar, Krishnendu Ghosh +3
Expert radiologists visually scan Chest X-Ray (CXR) images, sequentially fixating on anatomical structures to perform disease diagnosis. An automatic multi-label classifier of dise…
Self-supervision via Controlled Transformation and Unpaired Self-conditioning for Low-light Image Enhancement
Aupendu Kar, Sobhan K. Dhara, Debashis Sen +1
Real-world low-light images captured by imaging devices suffer from poor visibility and require a domain-specific enhancement to produce artifact-free outputs that reveal details.…
Histopathological Image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization
Vaibhav Khamankar, Sutanu Bera, Saumik Bhattacharya +2
Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in…
Self Supervised Low Dose Computed Tomography Image Denoising Using Invertible Network Exploiting Inter Slice Congruence
Sutanu Bera, Prabir Kumar Biswas
The resurgence of deep neural networks has created an alternative pathway for low-dose computed tomography denoising by learning a nonlinear transformation function between low-dos…
Sub-Aperture Feature Adaptation in Single Image Super-resolution Model for Light Field Imaging
Aupendu Kar, Suresh Nehra, Jayanta Mukhopadhyay +1
With the availability of commercial Light Field (LF) cameras, LF imaging has emerged as an up and coming technology in computational photography. However, the spatial resolution is…
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition
Manashi Chakraborty, Aritri Chakraborty, Prabir Kumar Biswas +1
This paper presents a texture aware end-to-end trainable iris recognition system, specifically designed for datasets like iris having limited training data. We build upon our previ…
Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal
Sutanu Bera, Prabir Kumar Biswas
Removing undesired reflections from a photo taken in front of glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches ha…
A Novel Approach for Earthquake Early Warning System Design using Deep Learning Techniques
Tonumoy Mukherjee, Chandrani Singh, Prabir Kumar Biswas
Earthquake signals are non-stationary in nature and thus in real-time, it is difficult to identify and classify events based on classical approaches like peak ground displacement,…
Noise Conscious Training of Non Local Neural Network powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising
Sutanu Bera, Prabir Kumar Biswas
The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radiation dose. However, reducing…
Progressive Update Guided Interdependent Networks for Single Image Dehazing
Aupendu Kar, Sobhan Kanti Dhara, Debashis Sen +1
Images with haze of different varieties often pose a significant challenge to dehazing. Therefore, guidance by estimates of haze parameters related to the variety would be benefici…
Lightweight Modules for Efficient Deep Learning based Image Restoration
Avisek Lahiri, Sourav Bairagya, Sutanu Bera +2
Low level image restoration is an integral component of modern artificial intelligence (AI) driven camera pipelines. Most of these frameworks are based on deep neural networks whic…
Unsupervised Pre-trained, Texture Aware And Lightweight Model for Deep Learning-Based Iris Recognition Under Limited Annotated Data
Manashi Chakraborty, Mayukh Roy, Prabir Kumar Biswas +1
In this paper, we present a texture aware lightweight deep learning framework for iris recognition. Our contributions are primarily three fold. Firstly, to address the dearth of la…
The Angel is in the Priors: Improving GAN based Image and Sequence Inpainting with Better Noise and Structural Priors
Avisek Lahiri, Arnav Kumar Jain, Prabir Kumar Biswas
Contemporary deep learning based inpainting algorithms are mainly based on a hybrid dual stage training policy of supervised reconstruction loss followed by an unsupervised adversa…
Faster Unsupervised Semantic Inpainting: A GAN Based Approach
Avisek Lahiri, Arnav Kumar Jain, Divyasri Nadendla +1
In this paper, we propose to improve the inference speed and visual quality of contemporary baseline of Generative Adversarial Networks (GAN) based unsupervised semantic inpainting…
A lightweight convolutional neural network for image denoising with fine details preservation capability
Sutanu Bera, Avisek Lahiri, Prabir Kumar Biswas
Image denoising is a fundamental problem in image processing whose primary objective is to remove the noise while preserving the original image structure. In this work, we proposed…
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network
Aupendu Kar, Prabir Kumar Biswas
Convolutional neural network (CNN) has achieved unprecedented success in image super-resolution tasks in recent years. However, the network's performance depends on the distributio…
Improved Techniques for GAN based Facial Inpainting
Avisek Lahiri, Arnav Jain, Divyasri Nadendla +1
In this paper we present several architectural and optimization recipes for generative adversarial network(GAN) based facial semantic inpainting. Current benchmark models are susce…
Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach
Avisek Lahiri, Abhinav Agarwalla, Prabir Kumar Biswas
With contemporary advancements of graphics engines, recent trend in deep learning community is to train models on automatically annotated simulated examples and apply on real data…
Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images
Avisek Lahiri, Charan Reddy, Prabir Kumar Biswas
Deep learning based object detectors require thousands of diversified bounding box and class annotated examples. Though image object detectors have shown rapid progress in recent y…
Retinal Vessel Segmentation under Extreme Low Annotation: A Generative Adversarial Network Approach
Avisek Lahiri, Vineet Jain, Arnab Mondal +1
Contemporary deep learning based medical image segmentation algorithms require hours of annotation labor by domain experts. These data hungry deep models perform sub-optimally in t…
Improving Consistency and Correctness of Sequence Inpainting using Semantically Guided Generative Adversarial Network
Avisek Lahiri, Arnav Jain, Prabir Kumar Biswas +1
Contemporary benchmark methods for image inpainting are based on deep generative models and specifically leverage adversarial loss for yielding realistic reconstructions. However,…
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Avisek Lahiri, Abhijit Guha Roy, Debdoot Sheet +1
Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy…