DerainGAN: Single image deraining using wasserstein GAN

Yadav, Sahil; Mehra, Aryan; Rohmetra, Honnesh; Ratnakumar, Rahul; Narang, Pratik

doi:10.1007/s11042-021-11442-6

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Cited by 13 publications

(1 citation statement)

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“…Haldar et al used a physics-based simulator to boost the performance of an object detection algorithm in rain [10]. Many synthetic rain generation techniques are deep learning-based and rely on the collection of rain and no-rain image pairs to use as training data [11,12].…”

Section: Related Workmentioning

confidence: 99%

The design and validation of a rain model for a simulated automotive environment

Brophy¹,

Deegan²,

Salado³

et al. 2023

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This paper presents the design of an accurate rain model for the commercially-available Anyverse automotive simulation environment. The model incorporates the physical properties of rain and a process to validate the model against real rain is proposed. Due to the high computational complexity of path tracing through a particle-based model, a second more computationally efficient model is also proposed. For the second model, the rain is modeled using a combination of a particle-based model and an attenuation field. The attenuation field is fine-tuned against the particle-only model to minimize the difference between the models.

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Section: Related Workmentioning

confidence: 99%