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基于混合注意力与门控倒残差模块的图像去雨网络

Image De-Raining Network Based on Hybrid Attention and Gate Inverted Residual Module

  • 摘要: 针对图像处理过程中通常存在细节信息丢失从而影响图像整体信息建模的问题, 提出一种基于混合注意力机制与门控机制的图像去雨网络. 首先混合注意力模块使用轴向注意力在图像横轴方向与竖轴方向上在像素层次聚合信息, 同时使用跨通道注意力增强全局建模能力, 最终通过融合注意力得到特征信息. 此外, 本文基于门控机制设计了包含倒残差模块的前馈网络, 前馈网络通过倒残差模块与门控机制提取和筛选局部信息特征. 多个公开数据集上的实验结果表明, 所提网络够有效地去除图像中的雨水条纹, 提高了图像清晰度与视觉效果; 与文中对比网络相比, 该网络在PSNR上提升0.81 dB, 显示出良好的性能优势.

     

    Abstract: Addressing the common issue of detail information loss during image processing, which impacts the overall information modeling of images, this paper proposes an image deraining network based on a hybrid attention mechanism and a gating mechanism. Initially, the hybrid attention module employs axial attention to aggregate information at the pixel level along the horizontal and vertical axes of the image, while enhancing global modeling capabilities through cross-channel attention. Subsequently, the fused attention results in feature information. Furthermore, this paper designs a feedforward network incorporating inverted residual blocks based on a gating mechanism. This feedforward network extracts and filters local information features through the combination of inverted residual blocks and the gating mechanism. Experimental results on multiple public datasets demonstrate that the proposed network can effectively remove rain streaks from images, enhancing image clarity and visual effects. Compared to the comparative networks mentioned in the paper, this network achieves a 0.81 dB improvement in PSNR, exhibiting a significant performance advantage.

     

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