Just Noticeable Defocus Blur Detection and Estimation

Jianping Shi, Li Xu, Jiaya Jia; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 657-665

Abstract


We tackle a fundamental problem to detect and estimate just noticeable blur (JNB) caused by defocus that spans a small number of pixels in images. This type of blur is common during photo taking. Although it is not strong, the slight edge blurriness contains informative clues related to depth. We found existing blur descriptors based on local information cannot distinguish this type of small blur reliably from unblurred structures. We propose a simple yet effective blur feature via sparse representation and image decomposition. It directly establishes correspondence between sparse edge representation and blur strength estimation. Extensive experiments manifest the generality and robustness of this feature.

Related Material


[pdf]
[bibtex]
@InProceedings{Shi_2015_CVPR,
author = {Shi, Jianping and Xu, Li and Jia, Jiaya},
title = {Just Noticeable Defocus Blur Detection and Estimation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}