7 papers
Dale meets Langevin: A Multiplicative Denoising Diffusion Model
Nishanth Shetty, Madhava Prasath, Chandra Sekhar Seelamantula
Exponentiated gradient descent (EGD), a biologically motivated optimisation algorithm that respects Dale's law, produces log-normally distributed synaptic weights at convergence, i…
Freq-DP Net: A Dual-Branch Network for Fence Removal using Dual-Pixel and Fourier Priors
Kunal Swami, Sudha Velusamy, Chandra Sekhar Seelamantula
Removing fence occlusions from single images is a challenging task that degrades visual quality and limits downstream computer vision applications. Existing methods often fail on s…
FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detection
Ashish Vashist, Qiranul Saadiyean, Suresh Sundaram +1
Foreign Object Debris (FOD) within aircraft fuel tanks presents critical safety hazards including fuel contamination, system malfunctions, and increased maintenance costs. Despite…
Cold-Diffusion Driven Downward Continuation of Gravity Data
Adarsh Jain, Pawan Bharadwaj, Chandra Sekhar Seelamantula
Gravity data can be better interpreted after enhancing high-frequency information via downward continuation. Downward continuation is an ill-posed deconvolution problem. It has bee…
3D-Image Reconstruction using MIMO-SAR FMCW Radar
Ayush Jha, Dhanireddy Chandrika, Chandra Sekhar Seelamantula +1
With the advancement of millimeter-wave radar technology, Synthetic Aperture Radar (SAR) imaging at millimeter-wave frequencies has gained significant attention in both academic re…
Weakly-Convex Regularization for Magnetic Resonance Image Denoising
Akash Prabakar, Abhishek Shreekant Bhandiwad, Abijith Jagannath Kamath +1
Regularization for denoising in magnetic resonance imaging (MRI) is typically achieved using convex regularization functions. Recently, deep learning techniques have been shown to…