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).
By Yang Jin, Guangyu Guo, Linghao Shen, Binglu Wang
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.
By Beibei Lin, Tingting Chen, Xin Zhang, Wenhao Zhao, Dongjun Li, Zifeng Yuan
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.
By Chenggong Li, Jinhao Liu, Caiyun Wu, Yidong Luo, Junchao Zhang, Degui Yang
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...
By Zhuangfan Huang, Xiaosong Li, Yang Liu, Tao Ye, Haishu Tan
arXiv:2609.12787v1 Announce Type: new
Abstract: Polarization image fusion combines the stable luminance and structural information of the total- intensity image S0 with the material-sensitive details...
By Zhuangfan Huang, Zhenyu Kuang, Gao Wang, Yang Liu, Haishu Tan, Xiaosong Li
arXiv:2610.27018v1 Announce Type: new
Abstract: Blurred vehicle contours and low contrast limit detection performance in infrared remote sensing imagery, while existing fusion methods may introduce p...
By Wenjuan Zhang, Xiangrui Peng, Zhen Li, Caimeng Yi, Xuesong Ma, Jianwei Wang, Jie Pan
arXiv:2609.01060v1 Announce Type: cross
Abstract: Compact snapshot hyperspectral cameras provide rich instantaneous spectral measurements for ground-level machine vision, but at lower spatial resolut...
By Mohamad Jouni, Aur\'elien Godet, Mauro Dalla Mura
Recovering high-quality images from degraded underwater observations is crucial for underwater exploration. Polarization observations show promise as an effective source of complementary physical cues...
NaCR: Visual Localization via NeRF-aided Camera Ray Regression proposes a unified framework that integrates Neural Radiance Fields (NeRF) with Camera Ray Regression (CRR) to improve visual localization accuracy. The method enhances the CRR baseline with three simple improvements, augments training data by synthesizing novel views from a pre‑trained NeRF, and employs a closed‑loop supervision pipeline that back‑propagates photometric rendering errors to refine predicted camera rays. A two‑stage training curriculum ensures stable convergence, and experiments on indoor and outdoor benchmarks show competitive accuracy with validated component efficacy.
By Yesheng Zhang, Xiang Dai, Xu Zhao, Chongyang Zhang
arXiv:2611.03114v2 Announce Type: replace
Abstract: 3D Gaussian Splatting (3DGS) has become a vital tool in novel view synthesis. 3D Gaussian functions with attributes like color, opacity, and scale...
By Yixiao Yang, Yu-Shen Liu, Zhizhong Han
The paper introduces TSR-ITNR, a two‑stage, self‑supervised framework for hyperspectral image super‑resolution that fuses high‑resolution multispectral and low‑resolution hyperspectral data. Stage 1 refines an implicit Tucker representation using a low‑rank spatial tensor and spectral basis, enhanced by a pretrained denoiser, to capture fine spatial details and spectral correlations. Stage 2 applies parameter‑free calibration to extract complementary corrections from both observations, preserving geometry and ensuring orthogonal complementarity, leading to superior reconstruction quality demonstrated on benchmark datasets and improved downstream segmentation performance.
By Liqian Yang, Xingchi Chen, Xinfeng Gui, Xiangyong Cao, Qianxin Yi
The paper introduces Deep Multitemporal Generalized Canonical Correlation Analysis (DeepMGCCA), an unsupervised deep multiview framework that learns deep pseudoinvariant features (DIFs) to harmonize surface reflectance from optical satellite imagery across different dates. By replacing linear projections with a shared convolutional encoder and employing iterative reweighting and a reconstruction regularizer, the method preserves genuine temporal variability while removing systematic biases. Experiments on Landsat‑8/9 and Sentinel‑2 data show that a monotone piecewise‑linear transform based on DIF quantiles reduces between‑date spectral angle by up to 2.54, RMSE by 0.015, and increases PSNR by up to 4.04 dB, demonstrating effective bias removal.
By Lino Garda Denaro, Chia-Hsiang Lin