Computer Science ›› 2018, Vol. 45 ›› Issue (8): 1-6.doi: 10.11896/j.issn.1002-137X.2018.08.001

• ChinaMM 2017 •     Next Articles

Deblurring for Imaging through Simple LensCombining Adaptive Gradient Sparsity and Interchannel Correlation

WANG, Xin-ling FU, Ying HUANG Hua   

  1. School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China
  • Received:2017-10-25 Online:2018-08-29 Published:2018-08-29

Abstract: Due to optical aberrations in imaging optics,the image taken from simple lensessuffers from severe artifacts and blurring.Aiming at this kind of blurring problem,this paper proposed a deblurring method combining adaptive gradient sparsity and interchannel correlation.This method restores every color channel of the blurred images separately through imposing different sparse priors on points in smooth areas and at edges and using interchannel correlation constraint,which uses edge information preserved in some channel to restore another channel.The simulation experiment results show that the proposed method can achieve better restoration in respect of image resolution and visual effect for blurred images through simple lens.

Key words: Adaptive gradient sparsity, Interchannel correlation, Non-blind deconvolution, Optical aberrations

CLC Number: 

  • TP391.41
[1]MAHAJAN V N.Aberration theory made simple.SPIE,1991.
[2]SHAN Q,JIA J,AGARWALA A.High-quality motion deblurring from a single image.ACM Transactions on Graphics,2008,27(3):1-10.
[3]KRISHNAN D,FERGUS R.Fast image deconvolution usinghyper-Laplacian priors[C]∥International Conference on Neural Information Processing Systems.Curran Associates Inc,2009:1033-1041.
[4]ZHAO Z G,CHEN Y Y,ZHAO Y,et al.Modeling deblurring based on edge prior model.Computer Science,2015,42(5):305-308.(in Chinese)赵志刚,陈莹莹,赵毅,等.基于边缘先验模型的运动去模糊.计算机科学,2015,42(5):305-308.
[5]TRUSSELL H,HUNT B.Image restoration of space variantblurs by sectioned methods[C]∥IEEE International Conference on ICASSP.IEEE,1978:196-198.
[6]TRUSSELL H,HUNT B.Sectioned methods for image restoration.IEEE Transactions on Acoustics Speech & Signal Processing,1978,26(2):157-164.BODEN A F,REDDING D C,HANISCH R J,et al.Massively Parallel Spatially-Variant Maximum Likelihood Image Restoration.Journal of The Optical Society of America A-Optics Ima-ge Science and Vision,1996,13:1537-1545.
[8]KEE E,PARIS S,CHEN S,et al.Modeling and removing spatially-varying optical blur[C]∥IEEE International Conference on Computational Photography.IEEE,2011:1-8.
[9]LEVIN A,FERGUS R,DURAND F,et al.Image and depthfrom a conventional camera with a coded aperture.Acm Transactions on Graphics,2007,26(3):70.
[10]RAHBAR K,FAEZ K.Blind correction of lens aberration using Zernike moments[C]∥IEEE International Conference on Image Processing(ICIP 2011).Brussels,Belgium,DBLP,2011:861-864.
[11]BOULT T E,WOLBERG G.Correcting chromatic aberrations using image warping[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1992(CVPR’92).IEEE,1992:684-687.
[12]KAUFMANN V,LADSTÄDTER R.Elimination of color fringes in digital photographs caused by lateral chromatic aberration//Proceedings of the XX International Symposium Cipa.2005.
[13]MALLON J,WHELAN P F.Calibration and removal of lateral chromatic aberration in images.Pattern Recognition Letters,2007,28(1):125-135.
[14]CHUNG S W,KIM B K,SONG W J.Detecting and eliminating chromatic aberration in digital images[C]∥IEEE International Conference on Image Processing.IEEE Press,2009:3861-3864.
[15]JOSHI N,ZITNICK C L,SZELISKI R,et al.Image deblurring and denoising using color priors[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2009(CVPR 2009).IEEE,2009:1550-1557.
[16]KANG S B.Automatic Removal of Chromatic Aberration from a Single Image[C]∥IEEE Conference on Computer Vision and Pattern Recognition,2007(CVPR’07).IEEE,2007:1-8.
[17]SCHULER C J,HIRSCH M,HARMELING S,et al.Non-stationary correction of optical aberrations[C]∥IEEE Internatio-nal Conference on Computer Vision.IEEE,2011:659-666.HEIDE F,ROUF M,HULLIN M B,et al.High-quality computational imaging through simple lenses.Acm Transactions on Graphics,2013,32(5):149.
[19]BONESKY T.Morozov’s discrepancy principle and Tikhonov-type functionals.Inverse Problems,2008,25(1):015015.
[20]ENGL H W,RAMLAU R.Regularization of Inverse Problems∥Regularization of Inverse Problems.Kluwer Academic Publishers,2000:347-366.
[21]TIKHONOV A N,ARSENIN V Y.Solutions of Ill-posed Problems.Mathematics of Computation,1977,32(144):491.
[22]CHAMBOLLE A,POCK T.A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging.Journal of Mathematical Imaging and Vision,2011,40(1):120-145.
[1] ZHANG Hong-bo, DONG Li-jia, PAN Yu-biao, HSIAO Tsung-chih, ZHANG Hui-zhen, DU Ji-xiang. Survey on Action Quality Assessment Methods in Video Understanding [J]. Computer Science, 2022, 49(7): 79-88.
[2] WU Lin, SUN Jing-yu. Multi-branch RA Capsule Network and Its Application in Image Classification [J]. Computer Science, 2022, 49(6): 224-230.
[3] WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[4] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[5] ZONG Di-di, XIE Yi-wu. Model Medial Axis Generation Method Based on Normal Iteration [J]. Computer Science, 2022, 49(6A): 764-770.
[6] WEI Qin, LI Ying-jiao, LOU Ping, YAN Jun-wei, HU Ji-wei. Face Recognition Method Based on Edge-Cloud Collaboration [J]. Computer Science, 2022, 49(5): 71-77.
[7] XING Yun-bing, LONG Guang-yu, HU Chun-yu, HU Li-sha. Human Activity Recognition Method Based on Class Increment SVM [J]. Computer Science, 2022, 49(5): 78-83.
[8] QU Zhong, CHEN Wen. Concrete Pavement Crack Detection Based on Dilated Convolution and Multi-features Fusion [J]. Computer Science, 2022, 49(3): 192-196.
[9] ZUO Jie-ge, LIU Xiao-ming, CAI Bing. Outdoor Image Weather Recognition Based on Image Blocks and Feature Fusion [J]. Computer Science, 2022, 49(3): 197-203.
[10] LENG Jia-xu, TAN Ming-pi, HU Bo, GAO Xin-bo. Video Anomaly Detection Based on Implicit View Transformation [J]. Computer Science, 2022, 49(2): 142-148.
[11] WEN Xiao-lin, LI Chang-lin, ZHANG Xin-yi, LIU Shang-song, ZHU Min. Visual Analysis Method of Blockchain Community Evolution Based on DPoS Consensus Mechanism [J]. Computer Science, 2022, 49(1): 328-335.
[12] ZHANG Qian, XIAO Li. Review of Visualization Drawing Methods of Flow Field Based on Streamlines [J]. Computer Science, 2021, 48(12): 1-7.
[13] LIU Zun-xiong, ZHU Cheng-jia, HUANG Ji, CAI Ti-jian. Image Super-resolution by Residual Attention Network with Multi-skip Connection [J]. Computer Science, 2021, 48(11): 258-267.
[14] LIU Yan, QIN Pin-le, ZENG Jian-chao. Multi-object Tracking Algorithm Based on YOLOv3 and Hierarchical Data Association [J]. Computer Science, 2021, 48(11A): 370-375.
[15] LUAN Xiao, LI Xiao-shuang. Face Anti-spoofing Algorithm Based on Multi-feature Fusion [J]. Computer Science, 2021, 48(11A): 409-415.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!