collaborators

11 papers

eess.IV2026

CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy

Su Yan, Clara Rodrigo Gonzalez, Vincent C. H. Leung +11

Super-resolution ultrasound via microbubble (MB) localisation and tracking, also known as ultrasound localisation microscopy (ULM), can resolve microvasculature beyond the acoustic…

eess.IV2025

Leveraging Overfitting for Low-Complexity and Modality-Agnostic Joint Source-Channel Coding

Haotian Wu, Gen Li, Pier Luigi Dragotti +1

This paper introduces Implicit-JSCC, a novel overfitted joint source-channel coding paradigm that directly optimizes channel symbols and a lightweight neural decoder for each sourc…

cs.CV2025

Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution

Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti

Diffusion models have transformed image synthesis through iterative denoising, by defining trajectories from noise to coherent data. While their capabilities are widely celebrated,…

cs.LG2025

On the Anisotropy of Score-Based Generative Models

Andreas Floros, Seyed-Mohsen Moosavi-Dezfooli, Pier Luigi Dragotti

We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models. To this end, we introduce the Score Anisotropy Directions (…

eess.IV2025

LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression

Haotian Wu, Gongpu Chen, Pier Luigi Dragotti +1

We introduce and validate the lottery codec hypothesis, which states that untrained subnetworks within randomly initialized networks can serve as synthesis networks for overfitted…

cs.CV2025

LatentINDIGO: An INN-Guided Latent Diffusion Algorithm for Image Restoration

Di You, Daniel Siromani, Pier Luigi Dragotti

There is a growing interest in the use of latent diffusion models (LDMs) for image restoration (IR) tasks due to their ability to model effectively the distribution of natural imag…