An Adaptive Pointwise P-norm Regularization for Image Deconvolution
Authors: 
Binbin Ma, Xiyuan Hu, Silong PengAuthors Info & Claims
Binbin Ma, Xiyuan Hu, Silong PengAuthors Info & ClaimsPages 12 - 16
Abstract
We propose a pointwise P-Norm based nonparametric image deconvolution method. In our algorithm, a pointwise p-norm based regular term is used to deal with the different types of regions in a blurry image such as edges, texture and smooth areas. A new hyper-parameter updating method is also adopted to improve the effectiveness and robustness of the proposed algorithm. The experimental results show that the quality of our restored images can outperform the results derived by other algorithms. In addition, our algorithm has a wide adaptation to different blur kernels.
References
[1]
Gonzalez R C, Woods R E. Image processing{J}. Digital image processing, 2007, 2.
[2]
Richardson W H. Bayesian-based iterative method of image restoration{J}. Journal of the Optical Society of America, 1972, 62(1): 55--59.
[3]
Lucy L B. An iterative technique for the rectification of observed distributions{J}. The astronomical journal, 1974, 79: 745.
[4]
Wang Y, Yang J, Yin W, et al. A new alternating minimization algorithm for total variation image reconstruction{J}. SIAM Journal on Imaging Sciences, 2008, 1(3): 248--272.
[5]
Goldstein T, Osher S. The split Bregman method for L1-regularized problems{J}. SIAM journal on imaging sciences, 2009, 2(2): 323--343.
[6]
Rodríguez P, Wohlberg B. Efficient minimization method for a generalized total variation functional{J}. IEEE Transactions on Image Processing, 2009, 18(2): 322--332.
[7]
Oliveira J P, Bioucas-Dias J M, Figueiredo M A T. Adaptive total variation image deblurring: a majorization-minimization approach{J}. Signal processing, 2009, 89(9): 1683--1693.
[8]
Bai Y, Cheung G, Liu X, et al. Blind Image Deblurring via Reweighted Graph Total Variation{J}. International Conference on Acoustics, Speech, and Signal Processing, 2017, 1822--1826.
[9]
Levin A, Fergus R, Frédo Durand, et al. Image and depth from a conventional camera with a coded aperture{J}. ACM Transactions on Graphics, 2007, 26(3):70.
[10]
Zhang F, Lu W, Liu H, et al. Natural image deblurring based on L0-regularization and kernel shape optimization{J}. Multimedia Tools and Applications, 2018, 77(20): 26239--26257.
[11]
Anwar S, Huynh C P, Porikli F. Image Deblurring with a Class-Specific Prior{J}. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2018, 99:1--1.
[12]
Wang Y, Feng H, Xu Z, et al. An improved Richardson-Lucy algorithm based on local prior{J}. Optics & Laser Technology, 2010, 42(5):845--849.
[13]
Yuan L, Sun J, Quan L, et al. Progressive inter-scale and intra-scale non-blind image deconvolution{J}. ACM Transactions on Graphics (TOG), 2008, 27(3):1--10.
[14]
Shan Q, Jia J, Agarwala A. High-quality motion deblurring from a single image{C}. ACM transactions on graphics(TOG), 2008, 27(3): 73.
[15]
Dong W, Shi G, Li X, et al. Image reconstruction with locally adaptive sparsity and nonlocal robust regularization{J}. Signal Processing: Image Communication, 2012, 27(10): 1109--1122.
[16]
Zhao J, Feng H, Xu Z, et al. An improved image deconvolution approach using local constraint{J}. Optics & Laser Technology, 2012, 44(2): 421--427.
[17]
Schuler C J, Burger H C, Harmeling S, et al. A Machine Learning Approach for Non-blind Image Deconvolution{C}. Computer Vision and Pattern Recognition, 2013, 1067--1074.
[18]
Zhang K, Zuo W, Gu S, et al. Learning Deep CNN Denoiser Prior for Image Restoration{J}.Computer Vision and Pattern Recognition, 2017, 2808--2817.
[19]
Galatsanos N P, Mesarovi V Z, Molina R, et al. Hyperparameter Estimation in Image Restoration Problems With Partially Known Blurs{J}. Optical Engineering, 2002, 41(8):1845--1854.
[20]
Simoes M, Almeida L B, Bioucas-Dias J, et al. A framework for fast image deconvolution with incomplete observations{J}. IEEE Transactions on Image Processing, 2016, 25(11): 5266--5280.
[21]
Zhou X, Molina R, Zhou F, et al. Fast iteratively reweighted least squares for lp regularized image deconvolution and reconstruction{C}. International Conference on Image Processing. 2014: 1783--1787.
[22]
Daubechies I, DeVore R, Fornasier M, et al. Iteratively reweighted least squares minimization for sparse recovery{J}. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 2010, 63(1): 1--38.
[23]
Song K S. A globally convergent and consistent method for estimating the shape parameter of a generalized Gaussian distribution{J}. IEEE Transactions on Information Theory, 2006, 52(2): 510--527.
[24]
Dabov K, Foi A, Katkovnik V, et al. Image restoration by sparse 3D transform-domain collaborative filtering{C}. Image Processing: Algorithms and Systems VI. International Society for Optics and Photonics, 2008, 6812: 681207.
Index Terms
- An Adaptive Pointwise P-norm Regularization for Image Deconvolution
Recommendations
High-quality non-blind image deconvolution with adaptive regularization
Non-blind image deconvolution is a process that obtains a sharp latent image from a blurred image when a point spread function (PSF) is known. However, ringing and noise amplification are inevitable artifacts in image deconvolution since perfect PSF ...
Image deconvolution using ℓ1 sparse regularization
ICIMCS '15: Proceedings of the 7th International Conference on Internet Multimedia Computing and ServiceThis paper studies sparse regularization image deconvolution scheme over the space of measures. This regularization method is the natural extension of the ℓ1 norm of vectors to the setting of measures. The proposed model is composed of data fitting term ...
Image compressive sensing via Truncated Schatten-p Norm regularization
Low-rank property as a useful image prior has attracted much attention in image processing communities. Recently, a nonlocal low-rank regularization (NLR) approach toward exploiting low-rank property has shown the state-of-the-art performance in ...
Comments
Information & Contributors
Information
Published In
February 2019
170 pages
ISBN:9781450362047
DOI:10.1145/3316551
Copyright © 2019 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 February 2019
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICDSP 2019
ICDSP 2019: 2019 3rd International Conference on Digital Signal Processing
February 24 - 26, 2019
Jeju Island, Republic of Korea
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 60Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)1
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in