  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.
