Single Image Rain Removal Based on Deep Learning and Symmetry Transform

Yang, Qing; Yu, Ming; Xu, Yan; Cen, Shixin

doi:10.3390/sym12020224

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Recent advances in machine learning approaches to image processing have led to the development of methods that enable both weather appearance transfer and generation -with many approaches aimed at weather removal (Yang et al, 2020). Generative Adversarial Networks (and their variants) have been successfully used for de-weathering (i.e.…”

Section: Related Work 21 Visually Realistic Weatheringmentioning

confidence: 99%

Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

Fursa¹,

Elias²,

Musat³

et al. 2021

Preprint

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Autonomous vehicles rely heavily upon their perception subsystems to 'see' the environment in which they operate. Unfortunately, the effect of varying weather conditions presents a significant challenge to object detection algorithms, and thus it is imperative to test the vehicle extensively in all conditions which it may experience. However, unpredictable weather can make real-world testing in adverse conditions an expensive and time consuming task requiring access to specialist facilities, and weatherproofing of sensitive electronics. Simulation provides an alternative to real world testing, with some studies developing increasingly visually realistic representations of the real world on powerful compute hardware. Given that subsequent subsystems in the autonomous vehicle pipeline are unaware of the visual realism of the simulation, when developing modules downstream of perception the appearance is of little consequence -rather it is how the perception system performs in the prevailing weather condition that is important. This study explores the potential of using a simple, lightweight image augmentation system in an autonomous racing vehicle -focusing not on visual accuracy, but rather the effect upon perception system performance. With minimal adjustment, the prototype system developed in this study can replicate the effects of both water droplets on the camera lens, and fading light conditions. The system introduces a latency of less than 8 ms using compute hardware that is well suited to being carried in the vehicle -rendering it ideally suited to real-time implementation that can be run during experiments in simulation, and augmented reality testing in the real world.

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Section: Related Work 21 Visually Realistic Weatheringmentioning

confidence: 99%

Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

Fursa¹,

Elias²,

Musat³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It seriously affects the image quality collected by the vision system, which can mislead the outdoor workers with inaccurate information [1,2]. erefore, it is necessary to solve the problem by removing rain from rainy images [3]. However, the rainy image background scene varies, and the density, shape, and direction of the rain are also diverse.…”

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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Single image rain removal via tetrolet transform and morphological decomposition

et al. 2023

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Single Image Rain Removal Based on Deep Learning and Symmetry Transform

Cited by 4 publications

References 8 publications

Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

Worsening Perception: Real-time Degradation of Autonomous Vehicle Perception Performance for Simulation of Adverse Weather Conditions

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

Single image rain removal via tetrolet transform and morphological decomposition

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