计算机科学
块(置换群论)
水准点(测量)
残余物
特征(语言学)
背景(考古学)
像素
人工智能
图像(数学)
模式识别(心理学)
遥感
计算机视觉
算法
地质学
数学
古生物学
语言学
哲学
几何学
大地测量学
作者
Tao Gao,Yuanbo Wen,Jing Zhang,Ting Chen
标识
DOI:10.1016/j.engappai.2023.107411
摘要
The dense rain accumulation in heavy rain can significantly wash out images and thus destroy the background details of images. Although existing deep rain removal models lead to improved performance for heavy rain removal, we find that most of them ignore the detail reconstruction accuracy of rain-free images. In this paper, we propose a dual-stage progressive enhancement network (DPENet-v2) to achieve effective deraining with structure-accurate rain-free images. Three main modules are included in our framework, namely a rain streaks removal network (R2Net), a details reconstruction network (DRNet) and a cross-stage feature interaction module (CFIM). The former aims to achieve accurate rain removal, and the latter is designed to recover the details of rain-free images. We introduce two main strategies within our networks to achieve trade-off between the effectiveness of deraining and the detail restoration of rain-free images. Firstly, a dilated dense residual block (DDRB) within the rain streaks removal network is presented to aggregate high/low level features of heavy rain. Secondly, an enhanced residual pixel-wise attention block (ERPAB) within the details reconstruction network is designed for context information aggregation. Meanwhile, CFIM learns the long-range dependencies and achieves cross-stage information communication. We also propose a comprehensive loss function to highlight the marginal and regional accuracy of rain-free images. Extensive experiments on benchmark public datasets show both efficiency and effectiveness of the proposed method in achieving structure-preserving rain-free images for heavy rain removal.
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