Progressive Rain Removal via a Recurrent Convolutional Network for Real Rain Videos

Lee, Kyu‐Ho; Ryu, Eunji; Kim, Jong-Ok

doi:10.1109/access.2020.3036680

Cited by 8 publications

(7 citation statements)

References 52 publications

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“…The success of convolutional neural networks (CNNs) in several research fields has inspired researchers to develop CNN-based image-denoising methods [14][15][16][17][18][19][20][21][22].…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

“…Following Yang et al [14][15][16] and Fu et al [17], several other authors proposed CNNbased methods [14][15][16][17][18]. These methods employed more advanced network architectures and the injection of new rain-related priors.…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

“…Lee et al [18] proposed a deep learning method for rain removal in videos based on a recurrent neural network (RNN) architecture. Pseudo-ground truth was generated from real rainy video sequences by temporally filtering through supervised learning instead of focusing on various shapes of rain streaks like conventional methods.…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

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Restoring Raindrops Using Attentive Generative Adversarial Networks

Yang

2021

Applied Sciences

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Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

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“…The success of convolutional neural networks (CNNs) in several research fields has inspired researchers to develop CNN-based image-denoising methods [14][15][16][17][18][19][20][21][22].…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

Section: Deep-learning-based Methodsmentioning

confidence: 99%

Section: Deep-learning-based Methodsmentioning

confidence: 99%

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Restoring Raindrops Using Attentive Generative Adversarial Networks

Yang

2021

Applied Sciences

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“…On the other hand, the authors in [30] divide the rainy input images into sparse ones and dense ones and model them separately based on a matrix decomposition. Moreover, with the dramatic development of DCNN [38], learning-based methods [20], [22], [26], [39] have signifi- cantly outperformed the traditional approaches. The work in [26] firstly considers the rain removal problem, including rain occlusions.…”

Section: A Video Derainingmentioning

confidence: 99%

Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images

Vu¹,

Gonzalez²,

Kim³

2021

Preprint

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Heavy rain removal from a single image is the task of simultaneously eliminating rain streaks and fog, which can dramatically degrade the quality of captured images. Most existing rain removal methods do not generalize well for the heavy rain case. In this work, we propose a novel network architecture consisting of three sub-networks to remove heavy rain from a single image without estimating rain streaks and fog separately. The first sub-net, a U-net-based architecture that incorporates our Spatial Channel Attention (SCA) blocks, extracts global features that provide sufficient contextual information needed to remove atmospheric distortions caused by rain and fog. The second sub-net learns the additive residues information, which is useful in removing rain streak artifacts via our proposed Residual Inception Modules (RIM). The third sub-net, the multiplicative sub-net, adopts our Channel-attentive Inception Modules (CIM) and learns the essential brighter local features which are not effectively extracted in the SCA and additive sub-nets by modulating the local pixel intensities in the derained images. Our three clean image results are then combined via an attentive blending block to generate the final clean image. Our method with SCA, RIM, and CIM significantly outperforms the previous state-of-the-art single-image deraining methods on the synthetic datasets, shows considerably cleaner and sharper derained estimates on the real image datasets. We present extensive experiments and ablation studies supporting each of our method's contributions on both synthetic and real image datasets.INDEX TERMS Heavy rain removal, Single image deraining, Image restoration, Deep learning.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Yao,

Shi,

et al. 2023

Remote Sensing

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With the continuous advancement of target detection technology in remote sensing, target detection technology in images captured by drones has performed well. However, object detection in drone imagery is still a challenge under rainy conditions. Rain is a common severe weather condition, and rain streaks often degrade the image quality of sensors. The main issue of rain streaks removal from a single image is to prevent over smoothing (or underclearing) phenomena. Aiming at the above problems, this paper proposes a deep learning (DL)-based rain streaks removal framework called GSDerainNet, which properly formulates the single image rain streaks removal problem; rain streaks removal is aptly described as a Gaussian Shannon (GS) filter-based image decomposition problem. The GS filter is a novel filter proposed by us, which consists of a parameterized Gaussian function and a scaled Shannon function. Two-dimensional GS filters exhibit high stability and effectiveness in dividing an image into low- and high-frequency parts. In our framework, an input image is first decomposed into a low-frequency part and a high-frequency part by using the GS filter. Rain streaks are located in the high-frequency part. We extract and separate the rain features of the high-frequency part through a deep convolutional neural network (CNN). The experimental results obtained on synthetic data and real data show that the proposed method can better suppress the morphological artifacts caused by filtering. Compared with state-of-the-art single image rain streaks removal methods, the proposed method retains finer image object structures while removing rain streaks.

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Progressive Rain Removal via a Recurrent Convolutional Network for Real Rain Videos

Cited by 8 publications

References 52 publications

Restoring Raindrops Using Attentive Generative Adversarial Networks

Restoring Raindrops Using Attentive Generative Adversarial Networks

Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images

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

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