A Light-Weight Self-Supervised Infrared Image Perception Enhancement Method

IF 2.6 3区 工程技术 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yifan Xiao, Zhilong Zhang, Zhouli Li
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引用次数: 0

Abstract

Convolutional Neural Networks (CNNs) have achieved remarkable results in the field of infrared image enhancement. However, the research on the visual perception mechanism and the objective evaluation indicators for enhanced infrared images is still not in-depth enough. To make the subjective and objective evaluation more consistent, this paper uses a perceptual metric to evaluate the enhancement effect of infrared images. The perceptual metric mimics the early conversion process of the human visual system and uses the normalized Laplacian pyramid distance (NLPD) between the enhanced image and the original scene radiance to evaluate the image enhancement effect. Based on this, this paper designs an infrared image-enhancement algorithm that is more conducive to human visual perception. The algorithm uses a lightweight Fully Convolutional Network (FCN), with NLPD as the similarity measure, and trains the network in a self-supervised manner by minimizing the NLPD between the enhanced image and the original scene radiance to achieve infrared image enhancement. The experimental results show that the infrared image enhancement method in this paper outperforms existing methods in terms of visual perception quality, and due to the use of a lightweight network, it is also the fastest enhancement method currently.
一种轻量级自监督红外图像感知增强方法
卷积神经网络(CNN)在红外图像增强领域取得了显著的成果。然而,对增强红外图像的视觉感知机制和客观评价指标的研究还不够深入。为了使主观评价和客观评价更加一致,本文采用感知度量来评价红外图像的增强效果。该感知度量模仿人类视觉系统的早期转换过程,使用增强图像与原始场景辐射度之间的归一化拉普拉斯金字塔距离(NLPD)来评价图像增强效果。在此基础上,本文设计了一种更有利于人类视觉感知的红外图像增强算法。该算法采用轻量级全卷积网络(FCN),以NLPD作为相似度量,通过最小化增强图像与原始场景辐射度之间的NLPD,以自我监督的方式训练网络,实现红外图像增强。实验结果表明,本文的红外图像增强方法在视觉感知质量方面优于现有方法,而且由于使用了轻量级网络,它也是目前最快的增强方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Electronics
Electronics Computer Science-Computer Networks and Communications
CiteScore
1.10
自引率
10.30%
发文量
3515
审稿时长
16.71 days
期刊介绍: Electronics (ISSN 2079-9292; CODEN: ELECGJ) is an international, open access journal on the science of electronics and its applications published quarterly online by MDPI.
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