arXiv subjects

Dipon Kumar Ghosh

Publications and source records attributed to Dipon Kumar Ghosh.

5 recordsLinked to original sources

arXiv2026

TIDES: Time-Derivative Event Simulation via Deformable Reconstruction

Event cameras emit asynchronous events in response to environmental appearance changes. The scarcity of real-world event datasets makes simulation essential. However, most simulators infer event timestamps from frame sequences, forcing many threshold crossings to share a small set of discrete times; a failure mode we term timestamp batching that worsens under fast motion and occlusion. We present TIDES, a continuous-time event simulator built on dynamic Gaussian splatting. Because TIDES operates on an explicit 3D scene representation with learnt geometry and motion, it can derive per-pixel intensity dynamics directly from the scene, rather than by differencing rendered frames. This enables accurate threshold-crossing prediction, including multiple crossings per rendering step, without temporal upsampling or frame interpolation. The same 3D scene model reveals where objects partially occlude one another; TIDES uses this to guide adaptive time stepping, concentrating computation only in regions where occlusion dynamics make simple models of brightness change unreliable. Finally, we model finite sensor bandwidth using a tile-level arbiter whose throughput, jitter, and event drops reproduce realistic sensor artifacts. Across paired RGB-event benchmarks, TIDES attains state-of-the-art event-stream fidelity. We also show that events simulated by TIDES transfer more effectively to real downstream tasks than competitors'.

cs.CV↗

arXiv2025

High-Performance Self-Powered Photoelectrochemical Detection Using Scalable InGaN/GaN Nanowire Arrays

Photoelectrochemical photodetectors (PEC-PDs) are promising owing to their simple, low-cost fabrication, self-powered operation, high photoresponse, and environmental sensitivity. In this work, we report for the first time the self-powered PEC photodetection characteristics of nanowire (NW) based green-emitting InGaN/GaN multiple quantum well (MQW) PEC-PDs, fabricated via a scalable top-down approach.The device exhibits strong UV sensitivity with a peak at 365 nm and an extended response into the visible region.Notably, a high photoresponsivity of 330 mA/W was achieved at a lower illumination intensity of 0.7 mW/cm2. Furthermore, the photodetector demonstrates fast, stable, and reproducible performance across varying biases and illumination conditions. These results suggest that InGaN/GaN MQW nanowire-based PEC photodetectors hold strong promise for scalable, efficient, and stable self-powered optoelectronic applications

physics.app-ph↗

arXiv2025

InGaN Nanopixel Arrays on Single Crystal GaN Substrate

Indium gallium nitride (InGaN) quantum well (QW) micro- and nanoscale light-emitting diodes (LEDs) are promising for next-generation ultrafast optical interconnects and augmented/virtual reality displays. However, scaling to nanoscale dimensions presents significant challenges, including enhanced nonradiative surface recombination, defect and/or dislocation-related emission degradation and nanoscale pixel contact formation. In this work, we demonstrate strain-engineered nanoscale blue LED pixels fabricated via top-down nanostructuring of an all-InGaN quantum well/barrier heterostructure grown by plasma-assisted molecular beam epitaxy (PAMBE) on significantly low dislocation-density single-crystal GaN substrates. Sidewall passivation using atomic layer deposition (ALD) of Al2O3 enables excellent diode behavior, including a high rectification ratio and extremely low reverse leakage. Monte Carlo analyses suggest almost 100% yield of completely dislocation-free active regions for 450 nm nanopixels. Electroluminescence measurements show bright blue emission with a peak external quantum efficiency (EQE) of 0.46%. Poisson Schrodinger simulations reveal partial strain relaxation in the QW, effectively mitigating the quantum confined Stark effect (QCSE). Additionally, finite-difference time-domain (FDTD) simulations confirm that the nanoscale geometry enhances light extraction efficiency by over 40% compared to planar designs, independent of substrate materials. These results establish a scalable pathway for dislocation free, high-brightness InGaN microLED arrays suitable for advanced display and photonic systems.

physics.app-ph↗

arXiv2022

Two-stream Multi-dimensional Convolutional Network for Real-time Violence Detection

The increasing number of surveillance cameras and security concerns have made automatic violent activity detection from surveillance footage an active area for research. Modern deep learning methods have achieved good accuracy in violence detection and proved to be successful because of their applicability in intelligent surveillance systems. However, the models are computationally expensive and large in size because of their inefficient methods for feature extraction. This work presents a novel architecture for violence detection called Two-stream Multi-dimensional Convolutional Network (2s-MDCN), which uses RGB frames and optical flow to detect violence. Our proposed method extracts temporal and spatial information independently by 1D, 2D, and 3D convolutions. Despite combining multi-dimensional convolutional networks, our models are lightweight and efficient due to reduced channel capacity, yet they learn to extract meaningful spatial and temporal information. Additionally, combining RGB frames and optical flow yields 2.2% more accuracy than a single RGB stream. Regardless of having less complexity, our models obtained state-of-the-art accuracy of 89.7% on the largest violence detection benchmark dataset.

cs.CV↗

arXiv2022

Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report

Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level vision tasks operate in the RAW domain (e.g. image denoising, white balance) due to its linear relationship with the scene irradiance, wide-range of information at 12bits, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public RGB datasets. This paper introduces the AIM 2022 Challenge on Reversed Image Signal Processing and RAW Reconstruction. We aim to recover raw sensor images from the corresponding RGBs without metadata and, by doing this, "reverse" the ISP transformation. The proposed methods and benchmark establish the state-of-the-art for this low-level vision inverse problem, and generating realistic raw sensor readings can potentially benefit other tasks such as denoising and super-resolution.

eess.IV↗