·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Figma announces new features to define how agents work on its platform3h◆Narrowing the Collaboration Gap, Probably4h◆AVA-Encoder: Towards Agent-Native Video Representation Learning4h◆Non-asymptotic Convergence of Average-reward Q-learning with Options4h◆Deep kernel hedging4h◆LLM2R: From Large Language Models to 3D Reassemblers4h◆Falsification-proof non-market allocation mechanisms4h◆Tiny Recursive Message Passing4h◆Learning Across Temporal Graph Representation Silos4h◆Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling4h◆Bayesian Learning to Select Fair Classifiers4h◆Continuum Attention4h◆On Inherent Privacy of Posterior Sampling: A Unified R\'enyi-Divergence Framework4h◆Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors4h◆SNACK: A Sequential Notation Framework for Probabilistic Graph Generation4h◆Trustworthy AI Must Account for Interactions4h◆Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing4h◆Learning a Fact Is Not Learning How to Retrieve It4h◆ClawLoop: Self-Improving Agents via Harness-Native Training Loop4h◆HERO: Hierarchical Evidential Reasoning Optimization for Radiology Report Generation via Reason-then-Summarize4h◆Figma announces new features to define how agents work on its platform3h◆Narrowing the Collaboration Gap, Probably4h◆AVA-Encoder: Towards Agent-Native Video Representation Learning4h◆Non-asymptotic Convergence of Average-reward Q-learning with Options4h◆Deep kernel hedging4h◆LLM2R: From Large Language Models to 3D Reassemblers4h◆Falsification-proof non-market allocation mechanisms4h◆Tiny Recursive Message Passing4h◆Learning Across Temporal Graph Representation Silos4h◆Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling4h◆Bayesian Learning to Select Fair Classifiers4h◆Continuum Attention4h◆On Inherent Privacy of Posterior Sampling: A Unified R\'enyi-Divergence Framework4h◆Discriminative Score Function: Turning Pretrained Models into Functional Generative Priors4h◆SNACK: A Sequential Notation Framework for Probabilistic Graph Generation4h◆Trustworthy AI Must Account for Interactions4h◆Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing4h◆Learning a Fact Is Not Learning How to Retrieve It4h◆ClawLoop: Self-Improving Agents via Harness-Native Training Loop4h◆HERO: Hierarchical Evidential Reasoning Optimization for Radiology Report Generation via Reason-then-Summarize4h◆
News/PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices
arxiv
PublishedSeptember 23, 2026 at 4:00 AM

PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2609.26474v1 Announce Type: cross Abstract: Scattered light is common in biomedical images, yet its removal remains challenging. The difficulty arises from three aspects: first, aligned scattered-light-free biomedical ground truth is often unavailable; second, scattering is coupled with weak i

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivNarrowing the Collaboration Gap, Probably4harxivAVA-Encoder: Towards Agent-Native Video Representation Learning4harxivNon-asymptotic Convergence of Average-reward Q-learning with Options4harxivDeep kernel hedging4h
The Bubble Brief
MON · WED · FRI

Read AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews