1 citations · 1 across the 4 of their papers we have counts for
6 papers
SymKL: A Unified Representation-Space Measure of Signal Degradation
Dhananjaya Jayasundara, Sudarshan Rajagopalan, Vishal M. Patel
Quantifying degradation between a pristine signal and its degraded observation is fundamental to signal processing, yet existing full-reference measures are inherently modality-spe…
Spectra: Multi-Scale Implicit Decomposition for Deepfake Detection
Dhananjaya Jayasundara, Kartik Narayan, Vishal M. Patel
Deepfakes increasingly undermine trust in digital media, yet existing detectors often struggle to generalize across unseen manipulation techniques, compression levels, and dataset…
Implicit Neural Representations: A Signal Processing Perspective
Dhananjaya Jayasundara, Vishal M. Patel
Implicit neural representations (INRs) mark a fundamental shift in signal modeling, moving from discrete sampled data to continuous functional representations. By parameterizing si…
SINR: Sparsity Driven Compressed Implicit Neural Representations
Dhananjaya Jayasundara, Sudarshan Rajagopalan, Yasiru Ranasinghe +2
Implicit Neural Representations (INRs) are increasingly recognized as a versatile data modality for representing discretized signals, offering benefits such as infinite query resol…
I-Diff: Structural Regularization for High-Fidelity Diffusion Models
Shakthi Perera, Dilum Fernando, H. L. P. Malshan +5
Denoising Diffusion Probabilistic Models (DDPMs) have significantly advanced generative AI, achieving impressive results in high-quality image and data generation. However, enhanci…
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness
Yasiru Ranasinghe, Kavinga Weerasooriya, Roshan Godaliyadda +6
In recent hyperspectral unmixing (HU) literature, the application of deep learning (DL) has become more prominent, especially with the autoencoder (AE) architecture. We propose a s…