文章基本信息

标题：Image Rain Removal Using Conditional Generative Networks Incorporating
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作者：Fangyan Zhang ; Xinzheng Xu ; Peng Wang 等
期刊名称：Journal of Computer and Communications
印刷版ISSN：2327-5219
电子版ISSN：2327-5227
出版年度：2022
卷号：10
期号：2
页码：72-82
DOI：10.4236/jcc.2022.102006
语种：English
出版社：Scientific Research Publishing
摘要：The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.
关键词：Attention MechanismConditional Production Adversarial NetworkLoss FunctionImage Deraining