DynGhost is a transformer-based architecture designed for dynamic ghost imaging, addressing two key limitations of prior methods: the lack of temporal coherence exploitation and the assumption of Gaussian noise. It alternates spatial and temporal attention blocks and incorporates a quantum-aware training framework that uses realistic detector simulations (SNSPDs, SPADs, SiPMs) and Anscombe variance-stabilizing normalization to handle Poissonian noise. Experiments on multiple benchmarks show that DynGhost surpasses traditional reconstruction methods and existing deep learning models, especially in dynamic and photon-starved scenarios.
arXiv:2603.06755v3 Announce Type: replace Abstract: We propose a quantum implicit neural representation (QINR)-based autoencoder (AE) and variational autoencoder (VAE) for image reconstruction and ge...
arXiv:2608.28674v1 Announce Type: new Abstract: Multi-exposure is an efficient way to capture real-world high-dynamic-range (HDR) scenes. However, HDR imaging suffers from severe ghosting artifacts i...
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...
QiT is a Quantum‑Inspired Transformer designed for visual recognition tasks. It replaces quantum neural network concepts with scalable classical operations: angle‑inspired encoding of image tokens, self‑attention over periodic features approximating quantum fidelity kernels, and gated multiplicative emulation of variational circuit interactions. The model achieves competitive performance on image‑classification benchmarks, matching a classical Transformer while avoiding the high runtime costs of simulated quantum models.
arXiv:2606. 13016v1 Announce Type: new Abstract: Spiking neural networks (SNNs) are promising for energy-efficient inference, and time-to-first-spike (TTFS) coding is especially attractive because each neuron fires at most once.
SlowFast‑SCI introduces a dual‑speed deep‑unfolding framework for spectral compressive imaging that combines a slow, pre‑trained backbone with a fast, test‑time adaptation stage. The slow phase distills a priors‑based model into a compact fast‑unfolding network, while the fast phase embeds lightweight modules that self‑supervise at test time without retraining the backbone. This design yields significant reductions in parameters and FLOPs, improves out‑of‑distribution PSNR by up to 5.79 dB, and accelerates adaptation four‑fold, all while remaining modular enough to integrate with any existing deep‑unfolding system.
Hybrid event-frame sensors combine an Event Vision Sensor (EVS) and an Active Pixel Sensor (APS) on a single chip, offering high dynamic range, low latency, and rich spatial intensity data. The paper introduces a unified statistics-based noise model that captures photon shot noise, dark current noise, fixed-pattern noise, and quantization noise for both APS and EVS pixels, and links EVS noise to illumination and dark current. It also presents a calibration pipeline to estimate these noise parameters from real data and proposes H-ESIM, a simulator that generates realistic RAW frames and events, validated on two hybrid sensors for tasks such as video frame interpolation and deblurring.
arXiv:2607. 22704v1 Announce Type: cross Abstract: Nuclear fusion has made significant progress in recent years and is expected to become one of the most important pathways to addressing global energy challenges.
arXiv:2607. 03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks.
Quantum Implicit Neural Representations for Novel View Synthesis introduces 3D Quantum Implicit Scene Representation (3D-QISR), a hybrid quantum‑classical radiance‑field model that replaces the classical NeRF backbone with parameterised quantum circuits. Two architectures are proposed: Full 3D-QISR, which uses a unified quantum state, and Dual‑Branch 3D-QISR, which separates spatial and view‑dependent embeddings to reduce complexity and improve scalability. Experiments on moderate‑resolution novel‑view synthesis benchmarks show that 3D‑QISR on simulated quantum hardware matches or outperforms classical baselines while using fewer than half the trainable parameters.
DAGS introduces a lightweight, attention‑free conditioning scheme that disentangles appearance and geometry for a frozen image diffusion transformer (DiT), enabling high‑fidelity, temporally stable renders with independent control. Two small convolutional encoders generate per‑frame conditioning features, which are injected as learned residuals into the image tokens, avoiding the quadratic cost of attention. Coupled with a recurrent lighting stabilizer and a training‑free temporal guidance term, DAGS transforms a per‑frame image model into a streaming renderer that outperforms real‑time denoisers and diffusion renderers in PSNR and temporal stability while requiring far less compute than path tracing.
arXiv:2609.37605v1 Announce Type: cross Abstract: Supervised deep learning has advanced sparse-view tomographic reconstruction. However, conventional models, which typically map filtered back-project...