738 citations · 1.1k across the 85 of their papers we have counts for
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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…
Towards automated mobile-phone-based plant pathology management
Nantheera Anantrasirichai, Sion Hannuna, Nishan Canagarajah
This paper presents a framework which uses computer vision algorithms to standardise images and analyse them for identifying crop diseases automatically. The tools are created to b…
Enhanced Spatially Interleaved Techniques for Multi-View Distributed Video Coding
Nantheera Anantrasirichai, Dimitris Agrafiotis
This paper presents a multi-view distributed video coding framework for independent camera encoding and centralized decoding. Spatio-temporal-view concealment methods are developed…
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…
Image Fusion via Sparse Regularization with Non-Convex Penalties
Nantheera Anantrasirichai, Rencheng Zheng, Ivan Selesnick +1
The L1 norm regularized least squares method is often used for finding sparse approximate solutions and is widely used in 1-D signal restoration. Basis pursuit denoising (BPD) perf…
DefectNET: multi-class fault detection on highly-imbalanced datasets
N. Anantrasirichai, David Bull
As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data. The prediction results of traditional networks give a bias tow…