Blur-Kernel Bound Estimation From Pyramid Statistics
Blur-Kernel Bound Estimation From Pyramid Statistics
复制标题
DOI：
10.1109/tcsvt.2015.2418585
复制
发表时间：
2016-05
影响因子：
8.4
通讯作者：
Shaoguo Liu;Haibo Wang;Jue Wang;Chunhong Pan
中科院分区：
文献类型：
--
作者：
Shaoguo Liu;Haibo Wang;Jue Wang;Chunhong Pan
This letter presents an approach for automatically estimating the spatial bound of the blur kernel in a motion-blurred image based on the statistics of multilevel image gradients. We observe that blur has a significant impact on the histogram of oriented gradients (HOGs) at higher levels of an image pyramid, but has much less of an impact at coarser levels. Based on this fact, we estimate the spatial bound of the unknown blur kernel using a learning-based approach. We first learn a generic pyramid HOG model from natural sharp images, then given an HOG pyramid of a blurry image, we predict the corresponding model of its latent sharp image. Finally, we learn another model to predict the spatial kernel bound from the difference between the observed and the predicted HOG pyramids. Experimental results show that the proposed method can estimate accurate blur kernel sizes, enabling existing blind deconvolution methods to achieve best possible results.
