1 citations · 1 across the 9 of their papers we have counts for
13 papers
Beyond Task Success: A Theory of Representation Reuse
Zibo Meng, Andrew C. M. Austin, Alin Achim
A learned representation is useful beyond its source task only if later tasks can make use of what the encoder has already learned. Source-task performance, however, gives no guara…
Diffusion Meets Unrolling: Compressive SAR Image Reconstruction with Interleaved Learned Corrections
Odysseas Pappas, Andrew C. M. Austin, Perla Mayo +2
Compressive Synthetic Aperture Radar (SAR) imaging, typically formulated as an inverse problem and solved with traditional iterative optimisation methods, can be very computational…
WBCBench 2026: A Challenge for Robust White Blood Cell Classification Under Class Imbalance
Xin Tian, Xudong Ma, Tianqi Yang +4
We present WBCBench 2026, an ISBI challenge and benchmark for automated WBC classification designed to stress-test algorithms under three key difficulties: (i) severe class imbalan…
Single Image Super-Resolution via Bivariate `A Trous Wavelet Diffusion
Maryam Heidari, Nantheera Anantrasirichai, Alin Achim
The effectiveness of super resolution (SR) models hinges on their ability to recover high frequency structure without introducing artifacts. Diffusion based approaches have recentl…
AWDiff: An a trous wavelet diffusion model for lung ultrasound image synthesis
Maryam Heidari, Nantheera Anantrasirichai, Steven Walker +2
Lung ultrasound (LUS) is a safe and portable imaging modality, but the scarcity of data limits the development of machine learning methods for image interpretation and disease moni…
An InSAR Phase Unwrapping Framework for Large-scale and Complex Events
Yijia Song, Juliet Biggs, Alin Achim +3
Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns. In earthquake-related deformation…
UnwrapDiff: A Conditional Diffusion Model for InSAR Phase Unwrapping
Yijia Song, Juliet Biggs, Alin Achim +3
Phase unwrapping is a fundamental problem in InSAR data processing, supporting geophysical applications such as deformation monitoring and hazard assessment. Its reliability is lim…
DMAT: An End-to-End Framework for Joint Atmospheric Turbulence Mitigation and Object Detection
Paul Hill, Zhiming Liu, Alin Achim +2
Atmospheric Turbulence (AT) degrades the clarity and accuracy of surveillance imagery, posing challenges not only for visualization quality but also for object classification and s…
Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction
Perla Mayo, Carolin M. Pirkl, Alin Achim +2
We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitio…
Modality Translation and Registration of MR and Ultrasound Images Using Diffusion Models
Xudong Ma, Nantheera Anantrasirichai, Stefanos Bolomytis +1
Multimodal MR-US registration is critical for prostate cancer diagnosis. However, this task remains challenging due to significant modality discrepancies. Existing methods often fa…
MR2US-Pro: Prostate MR to Ultrasound Image Translation and Registration Based on Diffusion Models
Xudong Ma, Nantheera Anantrasirichai, Stefanos Bolomytis +1
The diagnosis of prostate cancer increasingly depends on multimodal imaging, particularly magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS). However, accurate regi…
Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
Paul Hill, Nantheera Anantrasirichai, Alin Achim +1
The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approach…
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…