论文标题
QSAM-NET：通过自我发项模块的四元神经网络去除雨条
QSAM-Net: Rain streak removal by quaternion neural network with self-attention module
论文作者
论文摘要
在遥感，图像或视频检索中捕获的图像在现实世界中捕获，室外监视受到天气不良的质量降低的质量。雨水和雾气等条件引入工件，使视觉分析具有挑战并限制高级计算机视觉方法的性能。对于需要快速响应的时间临界应用，开发自动去除降雨的算法而不降低图像内容的质量变得至关重要。本文旨在开发一种新型的四个季节多阶段多尺度神经网络，该神经网络具有一个称为QSAM-NET的自发模块，以消除雨条。该算法的新颖性在于，在先前的方法上，它需要更少的参数为3.98，同时提高视觉质量。这是通过对合成和现实世界雨图像的广泛评估和基准测试来证明的。 QSAM-NET的此功能使网络适合在边缘设备和需要接近实时性能的应用程序上实现。实验表明，通过提高图像的视觉质量来证明这一点。此外，对象检测准确性和训练速度也得到提高。
Images captured in real-world applications in remote sensing, image or video retrieval, and outdoor surveillance suffer degraded quality introduced by poor weather conditions. Conditions such as rain and mist, introduce artifacts that make visual analysis challenging and limit the performance of high-level computer vision methods. For time-critical applications where a rapid response is necessary, it becomes crucial to develop algorithms that automatically remove rain, without diminishing the quality of the image contents. This article aims to develop a novel quaternion multi-stage multiscale neural network with a self-attention module called QSAM-Net to remove rain streaks. The novelty of this algorithm is that it requires significantly fewer parameters by a factor of 3.98, over prior methods, while improving visual quality. This is demonstrated by the extensive evaluation and benchmarking on synthetic and real-world rainy images. This feature of QSAM-Net makes the network suitable for implementation on edge devices and applications requiring near real-time performance. The experiments demonstrate that by improving the visual quality of images. In addition, object detection accuracy and training speed are also improved.
