64 citations · 138 across the 14 of their papers we have counts for
8 papers · 1 filter
UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots
Boxiao Yu, Jiayi Wu, Md Jahidul Islam
In this paper, we present a fast monocular depth estimation method for enabling 3D perception capabilities of low-cost underwater robots. We formulate a novel end-to-end deep visua…
A Generative Approach for Detection-driven Underwater Image Enhancement
Chelsey Edge, Md Jahidul Islam, Christopher Morse +1
In this paper, we introduce a generative model for image enhancement specifically for improving diver detection in the underwater domain. In particular, we present a model that int…
SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots
Md Jahidul Islam, Ruobing Wang, Junaed Sattar
This paper presents a holistic approach to saliency-guided visual attention modeling (SVAM) for use by autonomous underwater robots. Our proposed model, named SVAM-Net, integrates…
IMU-Assisted Learning of Single-View Rolling Shutter Correction
Jiawei Mo, Md Jahidul Islam, Junaed Sattar
Rolling shutter distortion is highly undesirable for photography and computer vision algorithms (e.g., visual SLAM) because pixels can be potentially captured at different times an…
Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
Md Jahidul Islam, Chelsey Edge, Yuyang Xiao +5
In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object…
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
Md Jahidul Islam, Peigen Luo, Junaed Sattar
In this paper, we introduce and tackle the simultaneous enhancement and super-resolution (SESR) problem for underwater robot vision and provide an efficient solution for near real-…
Fast Underwater Image Enhancement for Improved Visual Perception
Md Jahidul Islam, Youya Xia, Junaed Sattar
In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate a…
Enhancing Underwater Imagery using Generative Adversarial Networks
Cameron Fabbri, Md Jahidul Islam, Junaed Sattar
Autonomous underwater vehicles (AUVs) rely on a variety of sensors - acoustic, inertial and visual - for intelligent decision making. Due to its non-intrusive, passive nature, and…