54 citations · 61 across the 3 of their papers we have counts for
6 papers
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…
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…
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,…
WEPSAM: Weakly Pre-Learnt Saliency Model
Avisek Lahiri, Sourya Roy, Anirban Santara +2
Visual saliency detection tries to mimic human vision psychology which concentrates on sparse, important areas in natural image. Saliency prediction research has been traditionally…