87 citations · 142 across the 12 of their papers we have counts for
13 papers
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors
P. Hill, N. Anantrasirichai, A. Achim +1
Atmospheric turbulence poses a challenge for the interpretation and visual perception of visual imagery due to its distortion effects. Model-based approaches have been used to addr…
Towards a Robust Framework for NeRF Evaluation
Adrian Azzarelli, Nantheera Anantrasirichai, David R Bull
Neural Radiance Field (NeRF) research has attracted significant attention recently, with 3D modelling, virtual/augmented reality, and visual effects driving its application. While…
A Topological Loss Function: Image Denoising on a Low-Light Dataset
Alexandra Malyugina, Nantheera Anantrasirichai, David Bull
Although image denoising algorithms have attracted significant research attention, surprisingly few have been proposed for, or evaluated on, noise from imagery acquired under real…
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
Odysseas Pappas, Juliet Biggs, David Bull +2
Monitoring of ground movement close to the rail corridor, such as that associated with landslips caused by ground subsidence and/or uplift, is of great interest for the detection a…
Analysis of Vision-based Abnormal Red Blood Cell Classification
Annika Wong, Nantheera Anantrasirichai, Thanarat H. Chalidabhongse +3
Identification of abnormalities in red blood cells (RBC) is key to diagnosing a range of medical conditions from anaemia to liver disease. Currently this is done manually, a time-c…
Contextual colorization and denoising for low-light ultra high resolution sequences
N. Anantrasirichai, David Bull
Low-light image sequences generally suffer from spatio-temporal incoherent noise, flicker and blurring of moving objects. These artefacts significantly reduce visual quality and, i…
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
Nantheera Anantrasirichai, Juliet Biggs, Krisztina Kelevitz +5
The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding canno…
Fast Depth Estimation for View Synthesis
Nantheera Anantrasirichai, Majid Geravand, David Braendler +1
Disparity/depth estimation from sequences of stereo images is an important element in 3D vision. Owing to occlusions, imperfect settings and homogeneous luminance, accurate estimat…
Atmospheric turbulence removal using convolutional neural network
Jing Gao, N. Anantrasirichai, David Bull
This paper describes a novel deep learning-based method for mitigating the effects of atmospheric distortion. We have built an end-to-end supervised convolutional neural network (C…
HABNet: Machine Learning, Remote Sensing Based Detection and Prediction of Harmful Algal Blooms
P. R. Hill, A. Kumar, M. Temimi +1
This paper describes the application of machine learning techniques to develop a state-of-the-art detection and prediction system for spatiotemporal events found within remote sens…
The application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Timeseries
N. Anantrasirichai, J. Biggs, F. Albino +1
Automated systems for detecting deformation in satellite InSAR imagery could be used to develop a global monitoring system for volcanic and urban environments. Here we explore the…
A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets
Nantheera Anantrasirichai, Juliet Biggs, Fabien Albino +1
Satellites enable widespread, regional or global surveillance of volcanoes and can provide the first indication of volcanic unrest or eruption. Here we consider Interferometric Syn…
Detecting Volcano Deformation in InSAR using Deep learning
N. Anantrasirichai, F. Albino, P. Hill +2
Globally 800 million people live within 100 km of a volcano and currently 1500 volcanoes are considered active, but half of these have no ground-based monitoring. Alternatively, sa…