  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}.
