Attentive Generative Adversarial Network for Raindrop Removal from a Single Image

Qian, Rui; Tan, Robby T.; Yang, Wenhan; Su, Jiajun; Liu, Jiaying

doi:10.48550/arxiv.1711.10098

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…Next, based on the research findings from references [ 24 , 25 , 26 ], we estimate the approximate diameters of raindrops corresponding to different rainfall intensities. When the intensity of rainfall is greater, the diameters of raindrops tend to be larger.…”

Section: Methodology Of Simulation-based Corruption-related Testing S...mentioning

confidence: 99%

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Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving

Hsiang,

Chen

2024

Sensors

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Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models’ tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various “similarity overlap threshold” parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

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Section: Methodology Of Simulation-based Corruption-related Testing S...mentioning

confidence: 99%

“…Ref. [ 25 ] examined a vision-based driver assistance system that performs well under clear weather conditions but experiences a significant drop in reliability and robustness during rainy weather. Raindrops tend to accumulate on the vehicle’s windshield, leading to failures in camera-based ADAS.…”

Section: Related Workmentioning

confidence: 99%

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving

Hsiang,

Chen

2024

Sensors

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“… is designed to calculate the global difference between the ground truth image and the highlight removal result ( Qian et al, 2018 ). We extract image features by VGG16 ( Simonyan & Zisserman, 2014 ) pretrained on ImageNet datasets, the perceptual loss as in Eq.…”

Section: Related Workmentioning

confidence: 99%

Recognition of multi-modal fusion images with irregular interference

Wang

Chen

Wang

2022

PeerJ Computer Science

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Recognizing tomatoes fruits based on color images faces two problems: tomato plants have a long fruit bearing period, the colors of fruits on the same plant are different; the growth of tomato plants generally has the problem of occlusion. In this article, we proposed a neural network classification technology to detect maturity (green, orange, red) and occlusion degree for automatic picking function. The depth images (geometric boundary information) information of the fruits were integrated to the original color images (visual boundary information) to facilitate the RGB and depth information fusion into an integrated set of compact features, named RD-SSD, the mAP performance of RD-SSD model in maturity and occlusion degree respectively reached 0.9147.

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“…The recent rapid progress of CNNs in computer vision tasks such as image classification, semantic segmentation (He et al 2017) and image restoration (Qian et al 2017) is partly due to the creation of large-scale dataset such as Im-ageNet (Russakovsky et al 2015b) etc. It is commonly believed that robust models usually require large-scale sufficient training data set or expensive computation to avoid over-fitting (Lee et al 2015).…”

Section: Related Workmentioning

confidence: 99%

Adversarial Noise Layer: Regularize Neural Network by Adding Noise

You

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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In this paper, we introduce a novel regularization method called Adversarial Noise Layer (ANL) and its efficient version called Class Adversarial Noise Layer (CANL), which are able to significantly improve CNN's generalization ability by adding carefully crafted noise into the intermediate layer activations. ANL and CANL can be easily implemented and integrated with most of the mainstream CNN-based models. We compared the effects of the different types of noise and visually demonstrate that our proposed adversarial noise instruct CNN models to learn to extract cleaner feature maps, which further reduce the risk of over-fitting. We also conclude that models trained with ANL or CANL are more robust to the adversarial examples generated by FGSM than the traditional adversarial training approaches.

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Attentive Generative Adversarial Network for Raindrop Removal from a Single Image

Cited by 8 publications

References 18 publications

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving

Recognition of multi-modal fusion images with irregular interference

Adversarial Noise Layer: Regularize Neural Network by Adding Noise

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