Demosaicing

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11 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

S3D-Mosaic: Subgradient-Guided Cross-Band Structure Transfer for Spectral Demosaicing
Oct 17, 2026Weiqiang Zhao, Xian Li, Yuzhe Gui +2Demosaicing
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
SegDem: Segmentation helps Demosaicing
Aug 8, 2026Ping Chen, Xiangming Wang, Yongyong Chen +5Image Reconstruction Transfer Learning
Harbin Institute of Technology, Shenzhen · Shanghai Jiao Tong University · Nanjing University (Suzhou)
Structural Guidance for Unified Joint Demosaicing and Denoising
Aug 7, 2026Qixin Zheng, Ping Chen, Qiangqiang Shen +1Computational Imaging Image Restoration
Harbin Institute of Technology (Shenzhen), Shenzhen, China
A Human-in-the-Loop Deep Learning Framework for Color Reconstruction of Lenticular Films
Aug 3, 2026Saptarshi Neil Sinha, Tiago Kleist, Giorgio TrumpyImage Reconstruction Demosaicing
Virtual and Augmented Reality, Fraunhofer IGD, Darmstadt, Germany · Colourlab, Norwegian University of Science and Technology, Gjøvik, Norway
CPDDNet: Color-Polarization Denoising and Demosaicking Network
Jul 1, 2026Qihang Zhang, Yusuke Monno, Masayuki Tanaka +1Image Reconstruction Image Restoration
Institute of Science Tokyo
An Extensible and Lightweight Unified Architecture for Demosaicing Pixel-bin Image Sensors
Jun 11, 2026Saurabh Kumar, Nutan Sairam YennetiLightweight Neural Networks Demosaicing
AI Computational Imaging Samsung Research Institute Bangalore
RPBA-Net: An Interpretable Residual Pyramid Bilateral Affine Network for RAW-Domain ISP Enhancement
May 5, 2026Yucheng Xin, Wu Chen, Xiang Chen +7Image Enhancement Residual Learning
School of Information Science and Engineering, Shandong Normal University, Jinan, China · National University of Defense Technology, Changsha, China · Nanjing University of Science and Technology, Nanjing, China +2
LatentBurst: A Fast and Efficient Multi Frame Super-Resolution for Hexadeca-Bayer Pattern CIS images
Apr 25, 2026Sangwook Baek, Vin Van Duong, Karam Park +1Image Reconstruction Image Super-Resolution
Visual Technology Team, Samsung Research, Seoul, Republic of Korea
PolarAPP: Beyond Polarization Demosaicking for Polarimetric Applications
Mar 24, 2026Yidong Luo, Chenggong Li, Yunfeng Song +4Multi-Task Learning Polarimetric Imaging
Zhejiang University, Hangzhou, China · School of Automation, Central South University, Changsha, China · School of Engineering, Westlake University, Hangzhou, China +1
Architectural Unification for Polarimetric Imaging Across Multiple Degradations
Mar 6, 2026Chu Zhou, Yufei Han, Junda Liao +6Demosaicing Polarimetric Imaging
Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo 101-8430, Japan. · Pattern Recognition and Intelligent System Laboratory, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing 100080, China, and also with the National Engineering Research Center of Visual Technology, School of Computer Science, Peking University, Beijing 100080, China.
Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truth
Mar 2, 2026Andrew Wang, Mike DaviesDemosaicing Computational Imaging
School of Engineering, University of Edinburgh

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S3D-Mosaic: Subgradient-Guided Cross-Band Structure Transfer for Spectral Demosaicing

Weiqiang Zhao, Xian Li, Yuzhe Gui, Tianzhu Liu+1

School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

Demosaicing

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Multispectral filter array (MSFA) imaging enables snapshot spectral acquisition, but its per-band sparse sampling causes severe spatial aliasing. Although missing spatial structures can be inferred across bands, wavelength-dependent contrast variations make direct cross-band transfer unreliable. Existing model-based methods lack explicit magnitude-invariant mechanisms, and learning-based methods require layout-specific training or per-image tuning, limiting generalization. To address these issues, we propose S3D-Mosaic, a model-based spectral demosaicing method built upon a subgradient-guided cross-band structure transfer framework. We first establish a subgradient-guided regularization framework with a generalized cosine-similarity interpretation, providing a convex mechanism for magnitude-invariant information transfer. Then we design a compact Directional Sparse Equivariant Difference Convolution (DSE-DConv) for band-level extraction of orientation-selective, high-frequency structure features at low computational cost. Instantiating the framework with DSE-DConv on spectrally adjacent bands yields a cross-band self-structure similarity regularizer for spectral demosaicing, which we optimize via a dual-scan ADMM algorithm that decouples the bidirectional dependency while propagating magnitude-robust structural information across bands. With DSE-DConv's two parameters trained once via self-supervision, S3D-Mosaic outperforms state-of-the-art unsupervised methods on CAVE and NTIRE 2022, and generalizes robustly to varying MSFA layouts, pan-mosaic fusion, and real-world UAV data with fixed reconstruction hyperparameters. The source code is available at https://github.com/bestwishes43/S3D-Mosaic.