Search Results for author:
Found 30 papers, 9 papers with code
RKFNet: A Novel Neural Network Aided Robust Kalman Filter
Driven by the filtering challenges in linear systems disturbed by non-Gaussian heavy-tailed noise, the robust Kalman filters (RKFs) leveraging diverse heavy-tailed distributions have been introduced.
On the Modelling of Ship Wakes in S-Band SAR Images and an Application to Ship Identification
We present a novel ship wake simulation system for generating S-band Synthetic Aperture Radar (SAR) images, and demonstrate the use of such imagery for the classification of ships based on their wake signatures via a deep learning approach.
RoTIR: Rotation-Equivariant Network and Transformers for Fish Scale Image Registration
In this work, we present 'Rotation-Equivariant network and Transformers for Image Registration' (RoTIR), a deep-learning-based method for the alignment of fish scale images captured by light microscopy.
OCT2Confocal: 3D CycleGAN based Translation of Retinal OCT Images to Confocal Microscopy
This marks the first attempt to exploit the inherent 3D information of OCT and translate it into the rich, detailed color domain of confocal microscopy.
DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images
In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis.
Robust Kalman Filters Based on the Sub-Gaussian $α$-stable Distribution
Motivated by filtering tasks under a linear system with non-Gaussian heavy-tailed noise, various robust Kalman filters (RKFs) based on different heavy-tailed distributions have been proposed.
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images
Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment.
ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results
Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists.
Detection of Parasitic Eggs from Microscopy Images and the emergence of a new dataset
Automatic detection of parasitic eggs in microscopy images has the potential to increase the efficiency of human experts whilst also providing an objective assessment.
High-resolution Coastline Extraction in SAR Images via MISP-GGD Superpixel Segmentation
High accuracy coastline/shoreline extraction from SAR imagery is a crucial step in a number of maritime and coastal monitoring applications.
Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor
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 and prevention of possible railway faults.
Optimal Transport-based Graph Matching for 3D retinal OCT image registration
Finally, registration along the direction orthogonal to the x-y plane (the z-direction) is guided by the segmentation of two important anatomical features peculiar to mouse b-scans, the Internal Limiting Membrane (ILM) and the hyaloid remnant (HR).
Unsupervised Image Fusion Using Deep Image Priors
The results are compared with state-of-the-art image fusion methods.
Sparse regularization with a non-convex penalty for SAR imaging and autofocusing
In this paper, SAR image reconstruction with joint phase error estimation (autofocusing) is formulated as an inverse problem.
Current Advances in Computational Lung Ultrasound Imaging: A Review
In the field of biomedical imaging, ultrasonography has become increasingly widespread, and an important auxiliary diagnostic tool with unique advantages, such as being non-ionising and often portable.
SAR Image Autofocusing using Wirtinger calculus and Cauchy regularization
In this paper, an optimization model using Cauchy regularization is proposed for simultaneous SAR image reconstruction and autofocusing.
Exploiting the Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery
In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes.
A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface
The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc.
Non-convex Super-resolution of OCT images via sparse representation
We propose a non-convex variational model for the super-resolution of Optical Coherence Tomography (OCT) images of the murine eye, by enforcing sparsity with respect to suitable dictionaries learnt from high-resolution OCT data.
Representation Learning via Cauchy Convolutional Sparse Coding
In representation learning, Convolutional Sparse Coding (CSC) enables unsupervised learning of features by jointly optimising both an \(\ell_2\)-norm fidelity term and a sparsity enforcing penalty.
A Generalized Gaussian Extension to the Rician Distribution for SAR Image Modeling
In this paper, we present a novel statistical model, $\textit{the generalized-Gaussian-Rician}$ (GG-Rician) distribution, for the characterization of synthetic aperture radar (SAR) images.
Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services.
Detection of Line Artefacts in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularization
In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients.
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based Penalty
We show that the proposed Cauchy-based penalty function leads to better image reconstruction results when compared to the reference penalty functions for all SAR imaging inverse problems in this paper.
Iterative Cauchy Thresholding: Regularisation with a heavy-tailed prior
The Cauchy distribution is known to be able to capture heavy-tails in the data, which are linked to sparse processes.
Image and Video Processing
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties
In this paper, we propose a proximal splitting methodology with a non-convex penalty function based on the heavy-tailed Cauchy distribution.
Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation
A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution.
Image Fusion via Sparse Regularization with Non-Convex Penalties
The performance of the proposed method is benchmarked against a number of state-of-the-art image fusion techniques and superior performance is demonstrated both visually and in terms of various assessment measures.
Ship Wake Detection in SAR Images via Sparse Regularization
We show that the GMC achieves the best results and we subsequently study the merits of the corresponding method in comparison to two state-of-the-art approaches for ship wake detection.
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects.
