Abstract
The performance of image deraining algorithms has been significantly improved by introducing deep learning-based methods. However, their network structures have become more complicated and diverse, making it difficult to strike a balance between rain removal performance and processing speed. To address the aforementioned issues, we innovatively propose the Lightweight Detail-fusion Progressive Network (LDPNet) for image deraining, which can obtain more detailed rain-free images with fewer parameters and faster running speed. First, we decompose the challenging deraining task into multi-stage subtasks that gradually recover the degraded images. An effective combination of dense connections and the Gate Recurrent Unit allows our network to not only reuse features within each stage but also to transfer information between stages. This structure can achieve good performance with fewer parameters. Second, we design a multi-scale detail extraction block for recovering and reconstructing image details, which enhances the processing of detailed information by obtaining features with different receptive fields. Its lightweight design is achieved based on a depth-separable structure. Furthermore, we integrate a lightweight coordinate attention mechanism to achieve precise localization perception of detailed information in the rain removal region, which effectively improves the rain removal effect. The experimental results of the comprehensive datasets demonstrate the great superiority of our algorithm.
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This work is supported by Beijing Natural Science Foundation (4232017).
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Ding, S., Zhu, Q., Zhu, W. (2023). A Lightweight Detail-Fusion Progressive Network for Image Deraining. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_7
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