• Electronics Optics & Control
  • Vol. 27, Issue 7, 77 (2020)
ZHU Min1, FANG Chao1, and QI Meibin2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3969/j.issn.1671-637x.2020.07.015 Cite this Article
    ZHU Min, FANG Chao, QI Meibin. Raindrop Removal in a Single Image Based on Conditional Generative Adversarial Networks[J]. Electronics Optics & Control, 2020, 27(7): 77 Copy Citation Text show less

    Abstract

    Rainy weather will greatly worsen the image quality and hinder the subsequent processing of the image.In order to realize raindrop removal in the image, a single-image raindrop removal method based on Conditional Generative Adversarial Networks (CGAN) is proposed.This method adopts the basic framework of CGAN, uses the raindrop image as additional condition information and adds Lipschitz constraints.The network model is trained by combining condition adversarial loss, content loss with perception loss to repair the raindrop area and reconstruct the image.The experimental results show that the proposed method has better raindrop removal effects than the existing algorithms, and can avoid image blurring on the basis of ensuring the raindrop removal effect.
    ZHU Min, FANG Chao, QI Meibin. Raindrop Removal in a Single Image Based on Conditional Generative Adversarial Networks[J]. Electronics Optics & Control, 2020, 27(7): 77
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