From Rain Generation to Rain Removal

Wang, Hong; Yue, Zongsheng; Xie, Qi; Zhao, Qinglin; Zheng, Yefeng; Meng, Deyu

doi:10.1109/cvpr46437.2021.01455

Cited by 84 publications

(60 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[8] , RR‐GAN [20], Lpnet [17], Chen et al. [28] , VRGNet (variational rain generation network) [15] and MPRNet(multi‐stage progressive restoration network) [19]. We download the official source codes.…”

Section: Methodsmentioning

confidence: 99%

“…To solve this problem, Wang et al. [15] explored the inner generation mechanism of rain streaks, and a complete Bayesian generation model was constructed. Later, Chen et al.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, to enrich the existing dataset, Wang et al. [15] built a complete Bayesian generative model by learning an internal generation mechanism of rain streak. We found for the existing synthetic datasets, such as Rain100H [5] and DID‐MDN [6], the fully supervised method still cannot obtain ideal recovery results, and there is much room for improvement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A two‐stage method for single image de‐raining based on attention smoothed dilated network

Fan

Zhao

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Rain can severely hamper the visibility of scene objects. Although existing deep learning methods have reported promising performance, they often fail to obtain satisfactory results in many practical situations, especially when the input image contains both rain streaks and haze-like degradation. In this paper, a new two-stage method based on attention smoothed dilated network (SDN) is proposed. Unlike most fully-supervised methods, the mixture of rain streaks and haze-like effects is considered in the model. The proposed method consists of two stages. First, a generative adversarial network guided by the rain-streak attention map is proposed to remove rain streaks, where a multi-stage attention module is used to accurately locate rain streaks in the generator. Second, haze-like effects are further removed through SDN with the same structure as the generator. Extensive experiments on multiple datasets show that the method outperforms the state-of-the-art in both objective evaluation and visual quality.

show abstract

Section: Methodsmentioning

confidence: 99%

“…To solve this problem, Wang et al. [15] explored the inner generation mechanism of rain streaks, and a complete Bayesian generation model was constructed. Later, Chen et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A two‐stage method for single image de‐raining based on attention smoothed dilated network

Fan

Zhao

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Histogram of oriented gradient (HOG) and autocorrelation loss are used to facilitate the orientation consistency and repress repetitive rain streaks. They trained the network all the way from drizzle to downpour rain Fusion [110] LiDAR [152] LiDAR [76] LiDAR [153] Others [154] LiDAR [155] LiDAR [156] Camera [157] Camera [158] Camera [159] Camera [160] Camera [161] Camera [162] Camera [163] Camera [164] LiDAR [165] LiDAR [166] LiDAR [128] LiDAR [29] Fusion [129] LiDAR [167] Fusion [168] LiDAR [169] LiDAR [170] Fusion [171] LiDAR [172] Camera [173] Camera [174] Camera [175] Camera [176] Camera [177] Camera [178] Camera [179] Camera [180] Camera [181] Camera [182] Camera [183] Camera [184] Camera [185] Camera [186] Fusion [187] Fusion [188] LiDAR [189] Camera [190] Camera…”

Section: Rainmentioning

confidence: 99%

“…Like common de-noising methods, a close loop of both generation and removal can present better performance. H. Wang et al [163] handled the single image rain removal (SIRR) task by first building a full Bayesian generative model for rainy images. The physical structure is constructed by parameters including direction, scale and thickness.…”

Section: Rainmentioning

confidence: 99%

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

Zhang¹,

Carballo²,

Yang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. Stateof-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions like V2X technology, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. By pointing out all kinds of major weather problems the autonomous driving field is currently facing, and reviewing both hardware and computer science solutions in recent years, this survey contributes a holistic overview on the obstacles and directions of ADS development in terms of adverse weather driving conditions.

show abstract

A review of video‐based rainfall measurement methods

Yan

Chen

et al. 2023

WIREs Water

View full text Add to dashboard Cite

Accurate and high spatiotemporal resolution rainfall observations are essential for hydrological forecasting and flood management, especially in urban hydrological applications. However, it is difficult for traditional rainfall gauges, weather radars, and satellites to accurately estimate rainfall while simultaneously capturing the spatial and temporal variability of rainfall well. In this context, video‐based rainfall measurement, a novel method, has the advantages of real‐time performance and low cost and may thus provide a new way to establish rainfall observation networks with high spatial and temporal resolution. In recent years, different algorithms have been developed to recognize raindrops and estimate rainfall from rainfall videos. It has been demonstrated that video‐based rainfall measurement methods can provide comprehensive rainfall information with fine spatial and temporal granularity. However, raindrop visibility and the depth of field effects are difficult to address. The motion blur effect of raindrops may result in substantial errors and uncertainties. A fundamental problem of video‐based rainfall measurements lies in locating raindrops and accurately calculating their actual size. Moreover, the effectiveness of deep learning‐based video rainfall measurement models is greatly influenced by the diversity of the training data. Therefore, enhancing the high robustness and accuracy of video‐based rainfall measurement algorithms and increasing the computational efficiency are paramount to further development, which are prerequisites for their application in practical rainfall monitoring and developing multicamera monitoring networks.This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes

show abstract

From Rain Generation to Rain Removal

Cited by 84 publications

References 42 publications

A two‐stage method for single image de‐raining based on attention smoothed dilated network

A two‐stage method for single image de‐raining based on attention smoothed dilated network

Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey

A review of video‐based rainfall measurement methods

Contact Info

Product

Resources

About