Next Article in Journal
Effects of tDCS on Real-Time BCI Detection of Pedaling Motor Imagery
Next Article in Special Issue
Super-Resolution for “Jilin-1” Satellite Video Imagery via a Convolutional Network
Previous Article in Journal
Click Access to a Cyclodextrin-Based Spatially Confined AIE Material for Hydrogenase Recognition
Previous Article in Special Issue
Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model
Open AccessArticle

Defocus Blur Detection and Estimation from Imaging Sensors

School of Computer Science and Technology, Xidian University, Xi’an 710071, Shannxi, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(4), 1135; https://doi.org/10.3390/s18041135
Received: 10 March 2018 / Revised: 30 March 2018 / Accepted: 31 March 2018 / Published: 8 April 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
Sparse representation has been proven to be a very effective technique for various image restoration applications. In this paper, an improved sparse representation based method is proposed to detect and estimate defocus blur of imaging sensors. Considering the fact that the patterns usually vary remarkably across different images or different patches in a single image, it is unstable and time-consuming for sparse representation over an over-complete dictionary. We propose an adaptive domain selection scheme to prelearn a set of compact dictionaries and adaptively select the optimal dictionary to each image patch. Then, with nonlocal structure similarity, the proposed method learns nonzero-mean coefficients’ distributions that are much more closer to the real ones. More accurate sparse coefficients can be obtained and further improve the performance of results. Experimental results validate that the proposed method outperforms existing defocus blur estimation approaches, both qualitatively and quantitatively. View Full-Text
Keywords: sparse representation; defocus blur; adaptive domain selection; compact dictionaries; nonlocal structure similarity; coefficients’ distributions sparse representation; defocus blur; adaptive domain selection; compact dictionaries; nonlocal structure similarity; coefficients’ distributions
Show Figures

Figure 1

MDPI and ACS Style

Li, J.; Liu, Z.; Yao, Y. Defocus Blur Detection and Estimation from Imaging Sensors. Sensors 2018, 18, 1135.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop