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Paper details
Number 1 - March 2022
Volume 32 - 2022
Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network
Thiyagarajan Jayaraman, Gowri Shankar Chinnusamy
Abstract
The visual appearance of outdoor captured images is affected by various weather conditions, such as rain patterns, haze, fog
and snow. The rain pattern creates more degradation in the visual quality of the image due to its physical structure compared
with other weather conditions. Also, the rain pattern affects both foreground and background image information. The
removal of rain patterns from a single image is a critical process, and more attention is given to remove the structural rain
pattern from real-time rain images. In this paper, we analyze the single image deraining problem and present a solution using
the dual stage deep rain streak removal convolutional neural network. The proposed single image deraining framework
primarily consists of three main blocks: a derain streaks removal CNN (derain SRCNN), a modified residual dense block
(MRDB), and a six-stage scale feature aggregation module (3SFAM). The ablation study is conducted to evaluate the
performance of various modules available in the proposed deraining network. The robustness of the proposed deraining
network is evaluated over the popular synthetic and real-time data sets using four performance metrics such as the peak
signal-to-noise ratio (PSNR), the feature similarity index (FSIM), the structural similarity index measure (SSIM), and
the universal image quality index (UIQI). The experimental results show that the proposed framework outperforms both
synthetic and real-time images compared with other state-of-the-art single image deraining approaches. In addition, the
proposed network takes less running and training time.
Keywords
single image deraining, deep learning, modified residual dense network, PyTorch