@article{M2EA50CC6,
title = "Perceptual Generative Adversarial Network for Single Image De-Snowing",
journal = "KIPS Transactions on Software and Data Engineering",
year = "2019",
issn = "2287-5905",
doi = "https://doi.org/10.3745/KTSDE.2019.8.10.403",
author = "Weiguo Wan/Hyo Jong Lee",
keywords = "Generative Adversarial Network, De-Snowing, U-Net, Image Synthesis, Residual Block",
abstract = "Image de-snowing aims at eliminating the negative influence by snow particles and improving scene understanding in images. In this paper, a perceptual generative adversarial network based a single image snow removal method is proposed. The residual U-Net is designed as a generator to generate the snow free image. In order to handle various sizes of snow particles, the inception module with different filter kernels is adopted to extract multiple resolution features of the input snow image. Except the adversarial loss, the perceptual loss and total variation loss are employed to improve the quality of the resulted image. Experimental results indicate that our method can obtain excellent performance both on synthetic and realistic snow images in terms of visual observation and commonly used visual quality indices."
}