arXiv AI
Oct 5

Consecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures

The paper introduces Consecutive Posterior Fusion Denoising Diffusion Null‑Space Models (CPF‑DDNM), an inference‑time method that fuses successive measurement‑aware estimates to better recover unobservable image structures in severely ill‑posed imaging inverse problems. CPF‑DDNM builds on DDNM’s range/null‑space decomposition, preserving measurement‑determined components while refining prior‑driven null‑space estimates. Experiments on sparse‑view CT, low‑dose CT, and medical image super‑resolution demonstrate consistent improvements over DDNM and competitive performance against other diffusion‑based inverse solvers.

By Elena Morotti, Davide Evangelista, Elena Loli Piccolomini
arXiv AI
Jul 9

Does AI Understand Imaging? A Systematic Benchmark of Agentic AI for Computational Imaging Tasks

arXiv:2607. 07189v1 Announce Type: new Abstract: Vision-language models (VLMs) and agentic AI have shown strong performance on semantic visual tasks, but it remains unclear whether they can handle the physics and inverse problems that underlie computational imaging.

By Ethan Chung, Chuanjun Zheng, Jasper Tan, Jingxi Li, Haopeng Zhang, Huaijin Chen