arXiv Computer Vision
Proximal-Only Transmission Matrix Recovery of an Arbitrarily Deformed Graded-Index Multimode Fiber
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arXiv Machine Learning
Jun 18
Self-attention-based non-linear basis transformations for compact latent space modelling of dynamic optical fibre transmission matrices
arXiv:2406. 07775v2 Announce Type: replace Abstract: Multimode optical fibres are hair-thin strands of glass that efficiently transport light.
arXiv Computer Vision
Sep 2
Prior-Guided Implicit Neural Representations for Single-Subject Diffusion MRI Super-Resolution
The paper introduces a transfer‑learning framework that pre‑trains an implicit neural representation (INR) on a high‑resolution diffusion MRI template and then adapts it to individual subjects through registration and fine‑tuning. This approach enables native single‑subject super‑resolution, achieving a 4× through‑plane up‑sampling from 5 mm to 1.25 mm on Human Connectome Project data. Compared to a recent baseline, the method reduces NRMSE by 36–49 % and increases FSIM by 24–43 %, while training 6× faster and outperforming other INR‑based techniques on both image quality and domain‑specific metrics.
arXiv Machine Learning
Sep 18
Learning-Based Reconstruction of Optical Properties in Bilayered Media from Single-distance Time-Resolved Reflectance Measurements
The paper presents a machine‑learning framework for reconstructing absorption and scattering coefficients in bilayered biological media from single‑distance, time‑resolved reflectance data. By training on a synthetic dataset generated with exact Monte Carlo simulations, the method outperforms traditional diffusion‑equation‑based inverse solvers in both speed and accuracy. It also estimates the dimensionality of the parameter space without prior knowledge of the number of layers, and suggests that future work could further improve accuracy using multi‑distance data.
arXiv Machine Learning
Jul 30
Challenges and proposed solutions in modeling multimodal medical data: A systematic review
arXiv:2505. 06945v5 Announce Type: replace Abstract: Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records.
arXiv Computer Vision
Sep 15
Zero-Shot Cross-Material Ptychographic Phase Reconstruction Using Deep Learning
arXiv:2609.13969v1 Announce Type: new Abstract: Ptychographic phase reconstruction is commonly formulated as an iterative inverse problem, requiring repeated object-probe updates and resulting in sub...
arXiv Machine Learning
Jun 30
Learned iterative networks: An operator learning perspective
arXiv:2512. 08444v2 Announce Type: replace-cross Abstract: Learned image reconstruction has become a pillar in computational imaging and inverse problems.