16 citations · 27 across the 5 of their papers we have counts for
5 papers
Orthogonal Transform based Generative Adversarial Network for Image Dehazing
Ahlad Kumar, Mantra Sanathra, Manish Khare +1
Image dehazing has become one of the crucial preprocessing steps for any computer vision task. Most of the dehazing methods try to estimate the transmission map along with the atmo…
Deep Learning Architecture Based Approach For 2D-Simulation of Microwave Plasma Interaction
Mihir Desai, Pratik Ghosh, Ahlad Kumar +1
This paper presents a convolutional neural network (CNN)-based deep learning model, inspired from UNet with series of encoder and decoder units with skip connections, for the simul…
Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN AutoEncoder
Subham Nagar, Ahlad Kumar
This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated wi…
Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution
Ahlad Kumar, Harsh Vardhan Singh
The recent outbreak of COVID-19 has motivated researchers to contribute in the area of medical imaging using artificial intelligence and deep learning. Super-resolution (SR), in th…
Orthogonal Features-based EEG Signal Denoising using Fractionally Compressed AutoEncoder
Subham Nagar, Ahlad Kumar, M. N. S. Swamy
A fractional-based compressed auto-encoder architecture has been introduced to solve the problem of denoising electroencephalogram (EEG) signals. The architecture makes use of frac…