Encoder-Free Multiaxis Physics-Aware Fusion Network for Remote Sensing Image Dehazing

计算机科学 编码器 人工智能 块(置换群论) 特征(语言学) 管道(软件) 特征学习 代表(政治) 计算机视觉 操作系统 法学 程序设计语言 哲学 几何学 政治 语言学 数学 政治学
作者
Yuanbo Wen,Tao Gao,Jing Zhang,Ziqi Li,Ting Chen
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:61: 1-15 被引量:20
标识
DOI:10.1109/tgrs.2023.3325927
摘要

Current methods for remote sensing image dehazing confront noteworthy computational intricacies and yield suboptimal dehazed outputs, thereby circumscribing their pragmatic applicability. To this end, we propose EMPF-Net, a novel encoder-free multi-axis physics-aware fusion network that exhibits both light-weighted characteristics and computational efficiency. In our pipeline, we contend that conventional u-shaped networks allocate substantial computational resources to encode haze-degraded features, which play a subordinate role in the reconstruction process. Consequently, our encoder stages solely incorporate down-sampling operations. To improve the representation efficiency and enhance the generalization capabilities, we devise a multi-axis partial queried learning block (MPQLB) that primarily concentrates on learning dimension-wise queries, instead of relying solely on strictly-correlated content of the input features. Furthermore, we augment the reconstruction procedure by incorporating ground truth supervision into each stage via a supervised cross-scale transposed attention module (SCTAM). It calculates attention maps under the guidance of clean images, thereby suppressing less informative features to propagate to the subsequent level. In addition, to address the challenge of ineffective intral-level feature fusion, which result in insufficient elimination of haze-degraded information and negatively impact the quality of reconstructed images, we introduce a physics-aware intra-level fusion module (PIFM). This module harnesses a physical inversion model to facilitate the intra-level feature interaction and alleviate the interference of dehazing-irrelevant information. Our proposed EMPF-Net is evaluated on 12 publicly available datasets, and the experimental results substantiate our superiority in terms of both metrical scores and visual quality, despite being equipped with a modest parameter count of 300 K. Our approach is readily accessible at https://github.com/chdwyb/EMPF-Net.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
谦让汝燕完成签到,获得积分10
刚刚
青衫完成签到 ,获得积分10
1秒前
NING完成签到 ,获得积分10
1秒前
Lucas应助夜色萨尔图采纳,获得10
1秒前
zhoull发布了新的文献求助10
1秒前
3秒前
pwang_ecust完成签到,获得积分10
4秒前
kourosz完成签到,获得积分10
5秒前
希望天下0贩的0应助韭黄采纳,获得10
5秒前
Jackie完成签到 ,获得积分10
7秒前
7秒前
三木完成签到 ,获得积分10
7秒前
8秒前
薄荷味完成签到 ,获得积分10
8秒前
9秒前
sen123完成签到,获得积分10
9秒前
米糖安完成签到,获得积分10
14秒前
小树完成签到 ,获得积分10
15秒前
15秒前
子春完成签到 ,获得积分10
15秒前
anders完成签到 ,获得积分10
18秒前
天水张家辉完成签到,获得积分10
19秒前
20秒前
c123完成签到 ,获得积分10
21秒前
科研完成签到 ,获得积分10
21秒前
火星上的泡芙完成签到,获得积分10
21秒前
辣椒小皇纸完成签到 ,获得积分10
22秒前
Bai发布了新的文献求助10
22秒前
西瓜以亦完成签到 ,获得积分10
22秒前
23秒前
银海里的玫瑰_完成签到 ,获得积分10
23秒前
H0neYvia完成签到 ,获得积分10
24秒前
CO2完成签到,获得积分10
25秒前
27秒前
清颜完成签到 ,获得积分10
28秒前
粗犷的灵松完成签到 ,获得积分10
28秒前
韭黄发布了新的文献求助10
29秒前
路人完成签到,获得积分0
29秒前
爆米花应助陈龙采纳,获得10
30秒前
不吃了完成签到 ,获得积分0
31秒前
高分求助中
The Mother of All Tableaux Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 2400
Ophthalmic Equipment Market by Devices(surgical: vitreorentinal,IOLs,OVDs,contact lens,RGP lens,backflush,diagnostic&monitoring:OCT,actorefractor,keratometer,tonometer,ophthalmoscpe,OVD), End User,Buying Criteria-Global Forecast to2029 2000
Optimal Transport: A Comprehensive Introduction to Modeling, Analysis, Simulation, Applications 800
Official Methods of Analysis of AOAC INTERNATIONAL 600
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 588
A new approach to the extrapolation of accelerated life test data 500
T/CIET 1202-2025 可吸收再生氧化纤维素止血材料 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3953529
求助须知:如何正确求助?哪些是违规求助? 3498988
关于积分的说明 11093633
捐赠科研通 3229626
什么是DOI,文献DOI怎么找? 1785674
邀请新用户注册赠送积分活动 869464
科研通“疑难数据库(出版商)”最低求助积分说明 801470