NDLI: Linearly uncorrelated principal component and deep convolutional image deblurring for natural images

Content Provider	IET Digital Library
Author	Jeyaprakash, Amudha Radhakrishnan, Sudhakar
Abstract	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.
Starting Page	49
Ending Page	56
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5209
Journal	IET Image Processing
Publisher Date	2018-10-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Blind Image Deblurring Method Computer Vision And Image Processing Technique Convolution Deep Convolutional Image Deblurring Deep Convolutional Neural Network Model Hard Thresholding Technique Image Enhancement Image Quality Metrics Image Restoration Image Segmentation Image Texture Linearly Uncorrelated Principal Component Extraction Low-rank Matrix Generation Low-rank Prior Algorithm LUPC-DC Method Matrix Algebra Natural Image Processing Neural Computing Technique Neural Nets Nonuniform Image Preprocessing Optical, Image And Video Signal Processing Peak-signal-to-noise Ratio Rate Principal Component Analysis Statistics Uniform Image Preprocessing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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