A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives

Liu, Siyuan; Ren, Wenqi; Wang, Feng; Araujo, Iago Breno; Tokuda, Eric K.; Hirata, Roberto; Cesar-Jr, Roberto M.; Wang, Zhangyang; Cao, Xiaochun

doi:10.1007/s11263-020-01416-w

Cited by 52 publications

(24 citation statements)

References 77 publications

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“…Previous surveys on the subject [49][50][51][52][53] all provide a similar categorization of the surveyed methods. In this survey paper, we particularly highlight the attributes of more recent methods; that is, methods that were published in the year 2020 onward.…”

Section: A Taxonomy Of the Dl-based Single Image Deraining Methodsmentioning

confidence: 99%

A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze

Karavarsamis

Gkika

Gkitsas

et al. 2022

Sensors

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This survey article is concerned with the emergence of vision augmentation AI tools for enhancing the situational awareness of first responders (FRs) in rescue operations. More specifically, the article surveys three families of image restoration methods serving the purpose of vision augmentation under adverse weather conditions. These image restoration methods are: (a) deraining; (b) desnowing; (c) dehazing ones. The contribution of this article is a survey of the recent literature on these three problem families, focusing on the utilization of deep learning (DL) models and meeting the requirements of their application in rescue operations. A faceted taxonomy is introduced in past and recent literature including various DL architectures, loss functions and datasets. Although there are multiple surveys on recovering images degraded by natural phenomena, the literature lacks a comprehensive survey focused explicitly on assisting FRs. This paper aims to fill this gap by presenting existing methods in the literature, assessing their suitability for FR applications, and providing insights for future research directions.

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Section: A Taxonomy Of the Dl-based Single Image Deraining Methodsmentioning

confidence: 99%

A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze

Karavarsamis

Gkika

Gkitsas

et al. 2022

Sensors

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“…This part mainly focuses on several classical single image rain removal methods. A more detailed introduction can be found in [44,22,36].…”

Section: Related Workmentioning

confidence: 99%

Structure-Preserving Deraining with Residue Channel Prior Guidance

Yi¹,

Li²,

Dai³

et al. 2021

Preprint

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Single image deraining is important for many high-level computer vision tasks since the rain streaks can severely degrade the visibility of images, thereby affecting the recognition and analysis of the image. Recently, many CNN-based methods have been proposed for rain removal. Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures. To solve this problem, we propose a Structure-Preserving Deraining Network (SPDNet) with RCP guidance. SPDNet directly generates high-quality rain-free images with clear and accurate structures under the guidance of RCP but does not rely on any rain-generating assumptions. Specifically, we found that the RCP of images contains more accurate structural information than rainy images. Therefore, we introduced it to our deraining network to protect structure information of the rain-free image. Meanwhile, a Wavelet-based Multi-Level Module (WMLM) is proposed as the backbone for learning the background information of rainy images and an Interactive Fusion Module (IFM) is designed to make full use of RCP information. In addition, an iterative guidance strategy is proposed to gradually improve the accuracy of RCP, refining the result in a progressive path. Extensive experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves new state-of-the-art results. Code: https://github.com/Joyies/SPDNet

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“…In this article, aiming at the lack of consideration of the relationship between rain features and background in current rain removal methods [18][19][20], we analyze the pixel features of the rain images, clean images, and rain streak images. Because of the obvious overlap in pixel statistics, it is difficult to separate them from pixel level by physical means.…”

Section: Introductionmentioning

confidence: 99%

Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method

Xue

2021

Mathematical Problems in Engineering

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Rain has an undesirable negative effect on the clarity of the collected images. In situation where images are captured in rain, it can lead to a loss of information and disability in reflecting real images of the situation. Consequently, rain has become an obstacle in outdoor scientific research studies. The reason why images captured in rain are difficult to process is due to the indistinguishable interactions between the background features and rain textures. Since current image data are only processed with the CNN (convolutional neural network) model, a trained neural network to remove rain and obtain clear images, the resulted images are either insufficient or excessive from standard results. In order to achieve more ideal results of clearer images, series of additional methods are taken place. Firstly, the LBP (local binary pattern) method is used to extract the texture features of rain in the image. Then, the CGAN (conditional generative adversarial network) model is constructed to generate rain datasets according to the extracted rain characteristics. Finally, the existing clear images, rain datasets generated by CGAN, as well as the images with rain are used for convolution operation to remove rain from the images, and the average value of PSNR (peak signal to noise ratio) can reach 38.79 by using this algorithm. Moreover, a large number of experiments are done and have proven that this joint processing method is able to successfully and effectively generate clear images despite the rain.

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A Comprehensive Benchmark Analysis of Single Image Deraining: Current Challenges and Future Perspectives

Cited by 52 publications

References 77 publications

A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze

A Survey of Deep Learning-Based Image Restoration Methods for Enhancing Situational Awareness at Disaster Sites: The Cases of Rain, Snow and Haze

Structure-Preserving Deraining with Residue Channel Prior Guidance

Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method

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