Open AccessLinearly uncorrelated principal component and deep convolutional image deblurring for natural images
Author(s) -
Jeyaprakash Amudha,
Radhakrishnan Sudhakar
Publication year - 2019
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.5209
Subject(s) - deblurring , artificial intelligence , pattern recognition (psychology) , computer science , principal component analysis , convolutional neural network , thresholding , convolution (computer science) , image (mathematics) , algorithm , mathematics , computer vision , image processing , image restoration , artificial neural network
Blind image deblurring of natural images still remains a demanding task. The traditional methods, pre‐processes the uniform and non‐uniform images with a deblurring algorithm and employs a low‐rank prior algorithm. The rich textures do not possess enough similar patches in the deblurring process and this loss results in noisy images. Also, computational efficiency gets compromised during the performance of the succeeding process. In this study, the authors propose a novel method called, linearly uncorrelated principal component and deep convolution (LUPC‐DC) for deblurring natural images. The natural images are first de‐correlated with which good similar patches are extracted to generate a low‐rank matrix by linearly uncorrelated principal component (PC) extraction. Then, the deep convolutional neural network model jointly extracts good similar patches and deblurs the first PCs. Eventually, good similar patches in the last PCs are suppressed using Hard Thresholding for computational efficiency. Analysis of concurrence performance of the algorithm confirms the viability of this method theoretically. In addition, simulation results and performance evaluations of image quality metrics are provided to assess the effectiveness of the proposed method. Moreover, the proposed method provides improvement in the peak‐signal‐to‐noise ratio rate, success rate and reduction in the computation time for image deblurring.