Navigating Uncertainty: Semantic-Powered Image Enhancement and Fusion

Jiaxin Yao, Yongqiang Zhao, Seong G. Kong, Xun Zhang

科研成果: 期刊稿件文章同行评审

8 引用 (Scopus)

摘要

The fusion of infrared and visible imagery plays a crucial role in environmental monitoring. Existing approaches aim to achieve high-quality perceptual results for human observers and robust outcomes for machine-based high-level tasks by adopting a joint design of fusion and segmentation. However, design constraints imposed by predetermined fusion rules limit the precision of high-level tasks, and drawbacks in the image domains to be fused are often overlooked. This letter presents a novel semantic-powered infrared and visible image fusion framework to address these issues. The key feature of our approach is the utilization of trainable gains and weights in enhancement and fusion processes, influenced solely by segmentation and serving as uncertainty parameters. We propose a two-stage training strategy: initially, training a combined enhancement and fusion network with random uncertainty parameters, followed by the estimation of semantic-driven uncertainty parameters. The enhancement and fusion process is optimized within the Laplacian pyramid framework to ensure efficient computation. Experimental results highlight the significance of modeling the fusion process with uncertainty for achieving satisfactory fusion and segmentation outcomes.

源语言英语
页(从-至)1164-1168
页数5
期刊IEEE Signal Processing Letters
31
DOI
出版状态已出版 - 2024

指纹

探究 'Navigating Uncertainty: Semantic-Powered Image Enhancement and Fusion' 的科研主题。它们共同构成独一无二的指纹。

引用此