  In computer vision applications, the visibility of the video content is
crucial to perform analysis for better accuracy. The visibility can be affected
by several atmospheric interferences in challenging weather-one of them is the
appearance of rain streak. In recent time, rain streak removal achieves lots of
interest to the researchers as it has some exciting applications such as
autonomous car, intelligent traffic monitoring system, multimedia, etc. In this
paper, we propose a novel and simple method by combining three novel extracted
features focusing on temporal appearance, wide shape and relative location of
the rain streak and we called it TAWL (Temporal Appearance, Width, and
Location) method. The proposed TAWL method adaptively uses features from
different resolutions and frame rates. Moreover, it progressively processes
features from the up-coming frames so that it can remove rain in the real-time.
The experiments have been conducted using video sequences with both real rains
and synthetic rains to compare the performance of the proposed method against
the relevant state-of-the-art methods. The experimental results demonstrate
that the proposed method outperforms the state-of-the-art methods by removing
more rain streaks while keeping other moving regions.
