From heavy rain removal to detail restoration: A faster and better network

• URL: http://arxiv.org/abs/2205.03553v3
• Date: Mon, 18 Dec 2023 23:04:51 GMT
• Title: From heavy rain removal to detail restoration: A faster and better network
• Authors: Yuanbo Wen, Tao Gao, Jing Zhang, Kaihao Zhang and Ting Chen
• Abstract summary: In this work, we introduce a simple dual-stage progressive enhancement network, denoted as DPENet, to achieve effective deraining. This approach comprises two key modules, a rain streaks removal network (R$2$Net) focusing on accurate rain removal, and a details reconstruction network (DRNet) designed to recover the textural details of rain-free images.
• Score: 26.60300982543502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The profound accumulation of precipitation during intense rainfall events can markedly degrade the quality of images, leading to the erosion of textural details. Despite the improvements observed in existing learning-based methods specialized for heavy rain removal, it is discerned that a significant proportion of these methods tend to overlook the precise reconstruction of the intricate details. In this work, we introduce a simple dual-stage progressive enhancement network, denoted as DPENet, aiming to achieve effective deraining while preserving the structural accuracy of rain-free images. This approach comprises two key modules, a rain streaks removal network (R$^2$Net) focusing on accurate rain removal, and a details reconstruction network (DRNet) designed to recover the textural details of rain-free images. Firstly, we introduce a dilated dense residual block (DDRB) within R$^2$Net, enabling the aggregation of high-level and low-level features. Secondly, an enhanced residual pixel-wise attention block (ERPAB) is integrated into DRNet to facilitate the incorporation of contextual information. To further enhance the fidelity of our approach, we employ a comprehensive loss function that accentuates both the marginal and regional accuracy of rain-free images. Extensive experiments conducted on publicly available benchmarks demonstrates the noteworthy efficiency and effectiveness of our proposed DPENet. The source code and pre-trained models are currently available at \url{https://github.com/chdwyb/DPENet}.

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