Image Deraining with Frequency-Enhanced State Space Model

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Dettagli Bibliografici
Pubblicato in:	arXiv.org (Dec 8, 2024), p. n/a
Autore principale:	Yamashita, Shugo
Altri autori:	Ikehara, Masaaki
Pubblicazione:	Cornell University Library, arXiv.org
Soggetti:	Image degradation Rain Machine learning Image enhancement Information management Natural language processing Information flow Image processing Artificial neural networks State space models
Accesso online:	Citation/Abstract Full text outside of ProQuest
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Descrizione
Abstract:	Removing rain degradations in images is recognized as a significant issue. In this field, deep learning-based approaches, such as Convolutional Neural Networks (CNNs) and Transformers, have succeeded. Recently, State Space Models (SSMs) have exhibited superior performance across various tasks in both natural language processing and image processing due to their ability to model long-range dependencies. This study introduces SSM to image deraining with deraining-specific enhancements and proposes a Deraining Frequency-Enhanced State Space Model (DFSSM). To effectively remove rain streaks, which produce high-intensity frequency components in specific directions, we employ frequency domain processing concurrently with SSM. Additionally, we develop a novel mixed-scale gated-convolutional block, which uses convolutions with multiple kernel sizes to capture various scale degradations effectively and integrates a gating mechanism to manage the flow of information. Finally, experiments on synthetic and real-world rainy image datasets show that our method surpasses state-of-the-art methods. Code is available at https://github.com/ShugoYamashita/DFSSM.
ISSN:	2331-8422
Fonte:	Engineering Database