Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors

Zhang, Kaihao; Li, Rongqing; Yu, Yanjiang; Luo, Wenhan; Li, Changsheng; Li, Hongdong

doi:10.48550/arxiv.2103.11298

Cited by 2 publications

(2 citation statements)

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“…Recently, deep learning achieves significant success in lowlevel vision tasks such as image super-resolution [41], [42], [43], deblurring [44], [45], deraining [46], [47], desnowing [48], [49], which also include dehazing [15], [16]. At present, there are two kinds of main ideas about the dehazing methods based on deep learning.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing

Zheng¹,

Li²,

Luo³

2021

Preprint

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Hazy images reduce the visibility of the image content, and haze will lead to failure in handling subsequent computer vision tasks. In this paper, we address the problem of image dehazing by proposing a dehazing network named T-Net, which consists of a backbone network based on the U-Net architecture and a dual attention module. And it can achieve multi-scale feature fusion by using skip connections with a new fusion strategy. Furthermore, by repeatedly unfolding the plain T-Net, Stack T-Net is proposed to take advantage of the dependence of deep features across stages via a recursive strategy. In order to reduce network parameters, the intra-stage recursive computation of ResNet is adopted in our Stack T-Net. And we take both the stage-wise result and the original hazy image as input to each T-Net and finally output the prediction of clean image. Experimental results on both synthetic and real-world images demonstrate that our plain T-Net and the advanced Stack T-Net perform favorably against the state-of-the-art dehazing algorithms, and show that our Stack T-Net could further improve the dehazing effect, demonstrating the effectiveness of the recursive strategy.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing

Zheng¹,

Li²,

Luo³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning achieves significant success in low-level vision tasks such as image superresolution [26,22,43], deblurring [69,70,30], dehazing [46,28], which also include deraining [33,62,11,12,58,61,34,9,45,74,65,67,66]. These methods learn a mapping between input rainy images and their corresponding clean version using CNN/RNN based models.…”

Section: Introductionmentioning

confidence: 99%

Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding

Luo

Ren

Wang

et al. 2020

Lecture Notes in Computer Science

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Rain is a common natural phenomenon. Taking images in the rain however often results in degraded quality of images, thus compromises the performance of many computer vision systems. Most existing de-rain algorithms use only one single input image and aim to recover a clean image. Few work has exploited stereo images. Moreover, even for single image based monocular deraining, many current methods fail to complete the task satisfactorily because they mostly rely on per pixel loss functions and ignore semantic information. In this paper, we present a Paired Rain Removal Network (PRRNet), which exploits both stereo images and semantic information.

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Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors

Cited by 2 publications

References 59 publications

T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing

T-Net: Deep Stacked Scale-Iteration Network for Image Dehazing

Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding

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