Video Deraining Using the Visual Properties of Rain Streaks

Abstract

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 such interference is the appearance of rain streaks. Recently, rain streak removal has achieved plenty of interest among researchers, as it has some exciting applications such as autonomous cars, intelligent traffic monitoring systems, multimedia, etc. In this paper, we propose a novel and simple method of rain streak removal by combining three novel extracted visual features focusing on the temporal appearance, wide shape and relative location of the rain streak. We called it the 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 upcoming frames so that it can remove rain in realtime. Experiments have been conducted using video sequences with both real rain and synthetic rain 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.

SARain-GAN: Spatial Attention Residual UNet Based Conditional Generative Adversarial Network for Rain Streak Removal

“…Despite its innovative approach, a potential drawback is the complexity of the autosearching mechanism, which may pose challenges in terms of computational efficiency and implementation simplicity. Islam et al [32] introduced a nove ltechnique for removing rain streaks named TAWL (Temporal Appearance, Width, and Location), which combines three novel visual features emphasizing temporal appearance, wide shape, and relative location of rain streaks. TAWL adaptively utilizes features from various frame rates and resolutions, enabling real-time rain removal, but it is necessary to perform additional testing to ensure robustness as there are chances of variations in performance across diverse rain intensities and patterns.…”

A review of video‐based rainfall measurement methods

“…Subsequent research has explored additional rain‐streak physical properties to eliminate false information, including chromatic characteristics (Liu et al, 2009), the spatiotemporal correlation between rain pixels (Chen & Hsu, 2013), the local phase congruency (Santhaseelan & Asari, 2014), the multidirectional and multiscale properties (Li et al, 2016), and similar and repetitive shape features (Li et al, 2018). Islam and Paul (2022) recently combined three rain streak features (duration, width, and relative position of rain streaks) to distinguish rain streaks from the background and moving area.…”

GSDerainNet: A Deep Network Architecture Based on a Gaussian Shannon Filter for Single Image Deraining

“…We briefly review video-based methods for removing rain streaks. Due to the interframe information that exists in video, it is easier to remove rain streaks in videos [6][7][8][9]. For example, Islam and Paul [6] proposed making a better video by exploiting rain appearance duration, shape, and location to remove rain streaks from a video.…”

“…Due to the interframe information that exists in video, it is easier to remove rain streaks in videos [6][7][8][9]. For example, Islam and Paul [6] proposed making a better video by exploiting rain appearance duration, shape, and location to remove rain streaks from a video. Abdel-Hakim [7] exploited the interframe information to put the problem in a convex optimization form and then used low-rank restoration to remove rain and snow from a video.…”