Creator:

Jayaraman, Thiyagarajan ; Chinnusamy, Gowri Shankar

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Title:

Performance analysis of a dual stage deep rain streak removal convolution neural network module with a modified deep residual dense network

Group publication title:

AMCS, volume 32 (2022)

Subject and Keywords:

single image deraining ; deep learning ; modified residual dense network ; PyTorch

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2022

Resource Type:

artykuł

DOI:

10.34768/amcs-2022-0009

Pages:

111-123

Source:

AMCS, volume 32, number 1 (2022) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego