arXiv:2609.22479v1 Announce Type: new
Abstract: We address the problem of recovering high-speed videos from dynamic scenes under extreme photon sparsity. Existing methods rely on aggregating photon d...
By Jerry Yan, Matteo Forlivesi, Bowen Tan, Andrew Xie, Siddharth Somasundaram, Sotiris Nousias
The paper introduces a self‑supervised approach for Poisson inverse imaging problems that leverages frozen CLIP RN50 features as a lightweight prior within an ADMM‑inspired unrolled solver. By decoupling data‑consistency from the prior and using a parameter‑efficient decoder, the method adapts foundation vision representations without extensive fine‑tuning. Experiments on Poisson CFA demosaicing and deblurring demonstrate competitive image quality, enhanced robustness to dataset and acquisition shifts, and self‑supervised performance close to that of supervised training.
By Laura C. Diaz-Delgado, Emmanuel Martinez, Henry Arguello
arXiv:2608.27584v1 Announce Type: new
Abstract: Autonomous systems rely on extracting information from light, yet remain brittle in extreme environments, from nighttime navigation to high-speed robot...
By Varun Sundar, Pavan Thodima, Sacha Jungerman, Mohit Gupta
Raw images inherently suffer from noise due to the stochastic nature of light and sensor hardware imperfections. As real photon counts fall, the ratio of this noise to the signal degrades; consequently, for low-light conditions, robust denoising is especially vital for high-quality results.
The paper introduces a state‑aware framework that reconstructs both foreground and hidden scenes from single‑photon LiDAR histograms affected by partially transmissive occluders. It classifies each ray into no‑return, single‑return, or dual‑return states, guiding a two‑head neural field that jointly refines waveform reconstruction and geometry localization. A new paired dataset of occluded and clean LiDAR captures validates the method, showing improved depth and point‑cloud accuracy over existing baselines.
By Ziting Wen, Runrong Deng, Zili Zhang, Haitao Zheng, Yuecong Xu, Xiaoqiang Ren, Guodong Shi, Kemi Ding
The paper investigates how to model dynamic system transfer functions for event-based vision sensors. It analyzes three temporal probes—step, linear ramp, and exponential ramp—within a linear shift‑invariant optical system to determine which probe best matches a specified modulation transfer function. The study finds that step probes are vulnerable to threshold mismatch but robust to photon shot‑noise, while ramp probes maintain accuracy even with high mismatch, and it proposes dynamic point spread functions as part of a unified forward model for computational neuromorphic imaging.