StawGAN: Structural-Aware Generative Adversarial Networks for Infrared Image Translation

被引：5
|
作者：
Sigillo, Luigi [1 ,2 ]
Grassucci, Eleonora [1 ]
Comminiello, Danilo [1 ]
机构：
[1] Sapienza Univ Rome, Dept Informat Engn Elect & Telecom, Rome, Italy
[2] Leonardo Labs, Rome, Italy
来源：
2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS | 2023年
关键词：
Image Modality Translation; Generative Adversarial Networks; Drone Images; Infrared Images;
D O I：
10.1109/ISCAS46773.2023.10181838
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper addresses the problem of translating night-time thermal infrared images, which are the most adopted image modalities to analyze night-time scenes, to daytime color images (NTIT2DC), which provide better perceptions of objects. We introduce a novel model that focuses on enhancing the quality of the target generation without merely colorizing it. The proposed structural aware (StawGAN) enables the translation of better-shaped and high-definition objects in the target domain. We test our model on aerial images of the DroneVeichle dataset containing RGB-IR paired images. The proposed approach produces a more accurate translation with respect to other stateof-the-art image translation models. The source code will be available after the revision process.
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收藏
页数：5
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