Paper Info
Title
Robust Nonparametric Distribution Transfer with Exposure Correction for Image Neural Style Transfer.
Abstract
Image neural style transfer is a process of utilizing convolutional neural networks to render a content image based on a style image. The algorithm can compute a stylized image with original content from the given content image but a new style from the given style image. Style transfer has become a hot topic both in academic literature and industrial applications. The stylized results of current existing models are not ideal because of the color difference between two input images and the inconspicuous details of content image. To solve the problems, we propose two style transfer models based on robust nonparametric distribution transfer. The first model converts the color probability density function of the content image into that of the style image before style transfer. When the color dynamic range of the content image is smaller than that of style image, this model renders more reasonable spatial structure than the existing models. Then, an adaptive detail-enhanced exposure correction algorithm is proposed for underexposed images. Based this, the second model is proposed for the style transfer of underexposed content images. It can further improve the stylized results of underexposed images. Compared with popular methods, the proposed methods achieve the satisfactory qualitative and quantitative results.
Year
DOI
Venue
2020
10.3390/s20185232
SENSORS
Keywords
DocType
Volume
robust nonparametric distribution transfer,exposure correction,neural style transfer
Journal
20
Issue
ISSN
Citations 
18
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Shuai Liu120332.40
Caixia Hong200.34
Jing He300.34
Zhiqiang Tian48120.68