arXiv Computer Vision
LG-PF: Lightweight Confidence-Guided Polarization Image Fusion
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arXiv Computer Vision
Sep 14
LGFN: Lightweight Gated RGB-Polarization Fusion with Modality-Availability Conditioning for Camouflaged Object Detection
arXiv:2609.12798v1 Announce Type: new Abstract: Camouflaged object detection (COD) is an important engineering task in intelligent optical perception, but it remains challenging when targets closely...
arXiv Computer Vision
Oct 7
PolarScale: A Physics-Grounded Benchmark for Radiometrically Consistent RGB-to-Stokes Estimation
PolarScale is a new benchmark that explicitly requires models to predict the radiometric scale needed for full Stokes reconstruction from a per-scene normalized total‑intensity image. It evaluates models on normalized Stokes components, AoLP/DoLP/DoCP, and a per‑scene scale, using metrics that include angular, self‑consistency, and physical‑bound checks. Across seven restoration‑based and generative backbones, the best restoration models achieve a 3.6‑4.3% mean relative error in scale estimation, outperforming a constant‑scale control and maintaining physical plausibility.
arXiv Computer Vision
Sep 10
DensePol: Dense-Angle Polarization Dataset for Learning-Based Polarimetric Vision
arXiv:2609.09359v1 Announce Type: new Abstract: Polarimetric vision is gaining increasing attention because it provides physical cues about scene shape, material, and reflection that are difficult to...
arXiv Computer Vision
Oct 21
Long-wave Infrared Polarimetric Remote Sensing Image Fusion for Vehicle Detection
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arXiv Computer Vision
Oct 19
UniPolar: Joint Image and Polarimetric Restoration under Compound Degradations via Physical Consistency Learning
UniPolar is a new framework for restoring images and polarimetric data from color polarization filter array (CPFA) images that suffer from both device- and environment-induced degradations. It uses two complementary streams to reconstruct four polarization-angle images and predict Stokes difference components, coordinating them with a Degradation Conditioned Cross Domain Interaction module that exchanges frequency-selective evidence and applies condition-adaptive feature correction. The method also enforces physical consistency at the output level, aligning image-derived and directly predicted Stokes components while regularizing angular fidelity and the physical feasibility of degree of linear polarization (DoLP).
arXiv Computer Vision
Sep 28
Learning Polarization Image Restoration with General Restoration Priors
The paper presents an all-in-one framework for restoring polarization images affected by multiple coupled degradations. It identifies the normalized Stokes representation as effective for separating intensity and polarization, and introduces a dual-branch architecture that uses pretrained general restoration priors for the intensity branch while transferring knowledge to the polarization branch via cross-domain feature transform. A composite-degradation benchmark is also established to support future research.