Local Blur Detection of Digital Images Based on Deep Learning
Received date: 2018-02-06
Online published: 2018-03-31
Blurring is generally a post-operation to conceal or remove the trace of tampering. In this paper, a new convolutional neural network model is proposed, and the corresponding network topology is presented to handle the problems in the detection of blur operations, such as Gaussian blur, average blur, or median blur. An information process layer is added into the conventional convolutional neural network to extract the residual features of fltering frequency domain, accordingly, improving the accuracy of blur detection between the frst-order and the second-order fltering operations. Experimental results demonstrate that the proposed method performs a higher accuracy in blur detection than traditional methods, and is able to discriminate between the common linear and nonlinear blur operations.
YANG Bin, ZHANG Tao, CHEN Xian-yi . Local Blur Detection of Digital Images Based on Deep Learning[J]. Journal of Applied Sciences, 2018 , 36(2) : 321 -330 . DOI: 10.3969/j.issn.0255-8297.2018.02.011
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