@inproceedings{b50bad49bb2c4047803a607b00aef53d,
title = "Cross-Modal Generative Adversarial Networks for IR-Guided Mosaicked Image Restoration",
abstract = "Image mosaicking is widely used for privacy protection but severely degrades visual information in affected regions. This research addresses the challenge of restoring these mosaicked areas in visual-spectrum images. We propose a novel approach that leverages co-registered infrared (IR) imagery, which often remains unaffected by such obfuscation, as a guiding modality. Our method employs a Generative Adversarial Network (GAN) architecture specifically designed to learn the mapping between IR features and corresponding visual content. The GAN is trained to inpaint the mosaicked visual patches by generating realistic details conditioned on the IR image data. Experimental evaluations show the proposed method effectively reconstructs plausible and visually coherent content within the mosaicked regions, significantly enhancing image interpretability. This work demonstrates the potential of cross-modal GANs for robust image restoration in challenging scenarios.",
keywords = "Cross-Modal Learning, Generative Adversarial Networks, Image Demosaicking, Image Restoration",
author = "Yanlei Wen and Xudong Yao and Gang Meng and Zhiguo Jiang and Fengying Xie and Haopeng Zhang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 20th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2025 ; Conference date: 09-08-2025 Through 10-08-2025",
year = "2026",
doi = "10.1007/978-981-95-4966-5\_12",
language = "英语",
isbn = "9789819549658",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "168--182",
editor = "Yongtian Wang and Yi Chen",
booktitle = "Image and Graphics Technologies and Applications - 20th Chinese Conference, IGTA 2025, Revised Selected Papers",
address = "德国",
}