Abstract
Underwater Image Enhancement (UIE) techniques aim to address the problem of underwater image degradation due to light absorption and scattering. In recent years, both Convolution Neural Network (CNN)-based and Transformer-based methods have been widely explored. In addition, combining CNN and Transformer can effectively combine global and local information for enhancement. However, this approach is still affected by the secondary complexity of the Transformer and cannot maximize the performance. Recently, the state-space model (SSM) based architecture Mamba has been proposed, which excels in modeling long distances while maintaining linear complexity. This paper explores the potential of this SSM-based model for UIE from both efficiency and effectiveness perspectives. However, the performance of directly applying Mamba is poor because local fine-grained features, which are crucial for image enhancement, cannot be fully utilized. Specifically, we customize the MambaUIE architecture for efficient UIE. Specifically, we introduce visual state space (VSS) blocks to capture global contextual information at the macro level while mining local information at the micro level. Also, for these two kinds of information, we propose a Dynamic Interaction Block (DIB) and Spatial feed-forward Network (SGFN) for intra-block feature aggregation. MambaUIE is able to efficiently synthesize global and local information and maintains a very small number of parameters with high accuracy. Experiments on UIEB datasets show that our method reduces GFLOPs by 67.4% (2.715G) relative to the SOTA method. To the best of our knowledge, this is the first UIE model constructed based on SSM that breaks the limitation of FLOPs on accuracy in UIE. The official repository of MambaUIE at this https URL.
Abstract (translated)
水下图像增强（UIE）技术旨在解决因光吸收和散射而导致的水下图像降解问题。近年来，基于卷积神经网络（CNN）和基于Transformer的方法已经得到了广泛探索。此外，结合CNN和Transformer可以有效地结合全局和局部信息进行增强。然而，这种方法仍然受到Transformer的二级复杂性的影响，无法实现最佳性能。最近，基于状态空间模型（SSM）的Mamba架构被提出，它在保持线性复杂性的同时表现出长距离建模能力。本文探讨了这种SSM-based模型在UIE方面的潜力和效果。然而，直接应用Mamba的结果性能较差，因为本地细粒度特征，这些特征对于图像增强至关重要，无法得到充分利用。具体来说，我们为MambaUIE架构定制了高效的UIE。具体来说，我们在宏观层面捕捉全局上下文信息，同时也在微观层面挖掘局部信息。此外，为了这两种信息，我们提出了动态交互块（DIB）和空间前馈网络（SGFN）用于块级特征聚合。MambaUIE能够有效地合成全局和局部信息，并具有非常小的参数数量，具有很高的准确性。在UIEB数据集上的实验表明，与最先进的UIE方法相比，我们的方法减少了67.4%的GFLOPs（2.715G）。据我们所知，这是基于SSM构建的第一种能够突破FLOPs限制的UIE模型。MambaUIE的官方仓库地址为：https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https://<https:/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URL
https://arxiv.org/abs/2404.13884
