87 citations · 142 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…