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
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI