Video Rain-Streaks Removal by Combining Data-Driven and Feature-Based Models

Islam, Muhammad Rafiqul; Paul, Manoranjan

doi:10.3390/s21206856

Cited by 4 publications

(2 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Islam et al [24] proposed a hybrid technique, where physical features and data-driven features of rain are combined to remove rain streaks in videos. Jiang et al [25,26] used the sparsity of rain streaks to remove rain in videos.…”

Section: Video Snow and Rain Removal Methodsmentioning

confidence: 99%

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Jia

et al. 2021

Sensors

View full text Add to dashboard Cite

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods.

show abstract

Section: Video Snow and Rain Removal Methodsmentioning

confidence: 99%

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Jia

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…However, these methods cannot remove dense rain streaks since adjacent frames in heavy rain do not contain enough rain-free information. Some supervised daytime video deraining methods (Zhang et al 2022;Kulkarni, Patil, and Murala 2021;Wang et al 2022a,b;Zhuang et al 2022;Xue et al 2020;Wang et al 2019;Islam and Paul 2021;Su et al 2023;Li et al 2023) can be directly used to address nighttime rain streaks. However, these methods also suffer from the domain gap problem since they rely on synthetic datasets for training.…”

Section: Related Workmentioning

confidence: 99%

NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction

Lin,

Jin,

Yan

et al. 2024

AAAI

View full text Add to dashboard Cite

Existing deep-learning-based methods for nighttime video deraining rely on synthetic data due to the absence of real-world paired data. However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts. Motivated by this, we introduce NightRain, a novel nighttime video deraining method with adaptive-rain-removal and adaptive-correction. Our adaptive-rain-removal uses unlabeled rain videos to enable our model to derain real-world rain videos, particularly in regions affected by complex light effects. The idea is to allow our model to obtain rain-free regions based on the confidence scores. Once rain-free regions and the corresponding regions from our input are obtained, we can have region-based paired real data. These paired data are used to train our model using a teacher-student framework, allowing the model to iteratively learn from less challenging regions to more challenging regions. Our adaptive-correction aims to rectify errors in our model's predictions, such as over-saturation and color shifts. The idea is to learn from clear night input training videos based on the differences or distance between those input videos and their corresponding predictions. Our model learns from these differences, compelling our model to correct the errors. From extensive experiments, our method demonstrates state-of-the-art performance. It achieves a PSNR of 26.73dB, surpassing existing nighttime video deraining methods by a substantial margin of 13.7%.

show abstract

Video Deraining Using the Visual Properties of Rain Streaks

Islam

Paul

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Video Rain-Streaks Removal by Combining Data-Driven and Feature-Based Models

Cited by 4 publications

References 58 publications

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering

NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction

Video Deraining Using the Visual Properties of Rain Streaks

Contact Info

Product

Resources

About