Discriminative Blur Detection Features
Refereed conference paper presented and published in conference proceedings


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其它資訊
摘要Ubiquitous image blur brings out a practically important question - what are effective features to differentiate between blurred and unblurred image regions. We address it by studying a few blur feature representations in image gradient, Fourier domain, and data-driven local filters. Unlike previous methods, which are often based on restoration mechanisms, our features are constructed to enhance discriminative power and are adaptive to various blur scales in images. To avail evaluation, we build a new blur perception dataset containing thousands of images with labeled ground-truth. Our results are applied to several applications, including blur region segmentation, deblurring, and blur magnification.
著者Shi JP, Xu L, Jia JY
會議名稱27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
會議開始日23.06.2014
會議完結日28.06.2014
會議地點Columbus
會議國家/地區美國
詳細描述IEEE
出版年份2014
月份1
日期1
出版社IEEE
頁次2965 - 2972
電子國際標準書號978-1-4799-5117-8
國際標準期刊號1063-6919
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence

上次更新時間 2021-11-05 於 00:36