Suresh Chandra Raikwar

Thapar Institute of Engineering & Technology, GLA University

Papers

Total Citations

H-Index

About

Suresh Chandra Raikwar is a researcher specializing in computer vision and underwater image processing, with a focus on enhancing visual data for autonomous systems. His major contributions lie in developing deep learning-based methods to correct color cast and haze in underwater images, which are critical for applications in oceanography, resource exploration, and marine engineering. Notably, his work on "F2UIE: feature transfer-based underwater image enhancement using multi-stack CNN" (2023, 9 citations) and "FCNN: fusion-based underwater image enhancement using multilayer convolution neural network" (2022, 8 citations) introduces lightweight, fusion-based architectures that improve image clarity for underwater robotics. These papers address the challenges of light scattering and absorption in aquatic environments, offering practical solutions for real-time deployment. Earlier, Raikwar explored salient region detection (2014, 3 citations), proposing a Poisson distribution-based method for identifying attention-grabbing image regions, with applications in robotics and data transmission. His cumulative citation impact, though modest, reflects a focused trajectory in advancing image enhancement techniques for challenging environments, making his work valuable for researchers in marine robotics and computer vision.