A blocked statistics method based on directional derivative

Authors:
Junli Li;Chengxi Chu;Gang Li;Yang Lou
Affiliations:
Visual Computing and Virtual Reality Key Laboratory of Sichuan Province, Sichuan Normal University, Chengdu, China;Information Science and Engineering College, Ningbo University, Ningbo, China;Information Science and Engineering College, Ningbo University, Ningbo, China;Information Science and Engineering College, Ningbo University, Ningbo, China
Venue:
WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
Year:
2012

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Robust identification of motion and out-of-focus blur parameters from blurred and noisy images

CVGIP: Graphical Models and Image Processing
Identification of blur parameters from motion blurred images

Graphical Models and Image Processing

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Abstract

The basic idea of identifying the motion blurred direction using the directional derivative is that the original image be an isotropic first-order Markov random process. However, the real effect of this method is not always good. There are many reasons, of which the main is that a lot of pictures do not meet the physical premises. The shapes of objects and texture of pictures would be vulnerably influenced for identifying. In this paper, according to the image characteristics of the local variance, we extract multiple blocks and identify the motion directions of the blocks to identify the motion blurred direction. Experimental results show that our method not only improve the identification accuracy, but also reduce the amount of computation.