Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (3): 569-580.doi: 10.1007/s11390-019-1927-7

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

Previous Articles     Next Articles

Defocus Hyperspectral Image Deblurring with Adaptive Reference Image and Scale Map

De-Wang Li, Lin-Jing Lai, Hua Huang*, Distinguished Member, IEEE   

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
  • Received:2019-01-03 Revised:2019-03-26 Online:2019-05-05 Published:2019-05-06
  • Contact: Hua Huang
  • About author:De-Wang Li received his B.S. degree in computer science from Tsinghua University, Beijing, in 2016. He is currently a Ph.D. candidate at the School of Computer Science and Technology in Beijing Institute of Technology, Beijing. His research interests include image processing and computer vision.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China under Grant No. 61425013.

Defocus blur is one of the primary problems among hyperspectral imaging systems equipped with simple lenses. Most of the previous deblurring methods focus on how to utilize structure information of a single channel, while ignoring the characteristics of hyperspectral images. In this work, we analyze the correlations and differences among spectral channels, and propose a deblurring framework for defocus hyperspectral images. First, we divide the hyperspectral image channels into two sets, and the set with less blur is treated as a group of spectral bases. Then, according to the inherent correlations of spectral channels, a reference image can be derived from the spectral bases to guide the restoration of blurry channels. Finally, considering the disagreement between the reference image and the ground truth, a scale map based on gradient similarity is introduced as a prior in the deblurring framework. The experimental results on public dataset demonstrate that the proposed method outperforms several image deblurring methods in both visual effect and quality metrics.

Key words: hyperspectral image; defocus blur; deblurring; reference image; scale map;

[1] Haboudane D, Miller J R, Pattey E et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies:Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 2004, 90(3):337-352.
[2] Martin M E, Wabuyele M B, Chen K et al. Development of an advanced hyperspectral imaging (HSI) system with applications for cancer detection. Annals of Biomedical Engineering, 2006, 34(6):1061-1068.
[3] Bioucas-Dias J M, Plaza A, Camps-Valls G et al. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2):6-36.
[4] Weiner N. Extrapolation, Interpolation, and Smoothing of Stationary Time Series:With Engineering Applications. MIT Press, 1964.
[5] Richardson W H. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America, 1972, 62(1):55-59.
[6] Fergus R, Singh B, Hertzmann A et al. Removing camera shake from a single photograph. ACM Transactions on Graphics, 2006, 25(3):787-794.
[7] Levin A, Fergus R, Durand F et al. Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics, 2007, 26(3):Article No. 70.
[8] Jia J. Single image motion deblurring using transparency. In Proc. the 2017 IEEE Computer Society Conference onComputer Vision and Pattern Recognition, June 2007, Article No. 51.
[9] Shan Q, Jia J, Agarwala A. High-quality motion deblurring from a single image. ACM Transactions on Graphics, 2008, 27(3):Article No. 73.
[10] Xu L, Jia J. Two-phase kernel estimation for robust motion deblurring. In Proc. the 11th European Conference on Computer Vision, September 2010, pp.157-170.
[11] Dabov K, Foi A, Katkovnik V et al. Image restoration by sparse 3D transform-domain collaborative filtering. In SPIE Proceedings Vol. 6812:Image Processing:Algorithms and Systems VI, Astola J T, Egiazarian K O, Dougherty E R (eds.), March 2008.
[12] Danielyan A, Katkovnik V, Egiazarian K. BM3D frames and variational image deblurring. IEEE Transactions on Image Processing, 2012, 21(4):1715-1728.
[13] Fortunato H E, Oliveira M M. Fast high-quality non-blind deconvolution using sparse adaptive priors. The Visual Computer, 2014, 30(6/7/8):661-671.
[14] Yan Y, Ren W, Guo Y et al. Image deblurring via extreme channels prior. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.6978-6986.
[15] Dong W, Zhang L, Shi G et al. Nonlocally centralized sparse representation for image restoration. IEEE Transactions on Image Processing, 2013, 22(4):1620-1630.
[16] Dong W, Zhang L, Shi G et al. Image deblurring and superresolution by adaptive sparse domain selection and adaptive regularization. IEEE Transactions on Image Processing, 2011, 20(7):1838-1857.
[17] Dong W, Shi G, Ma Y et al. Image restoration via simultaneous sparse coding:Where structured sparsity meets Gaussian scale mixture. International Journal of Computer Vision, 2015, 114(2-3):217-232.
[18] Zhang J, Gai D, Zhang X et al. Multi-example featureconstrained back-projection method for image superresolution. Computational Visual Media, 2017, 3(1):73-82.
[19] Xu L, Ren J S J, Liu C et al. Deep convolutional neural network for image deconvolution. In Proc. the 2014 Annual Conference on Neural Information Processing Systems, December 2014, pp.1790-1798.
[20] Jin M, Roth S, Favaro P. Noise-blind image deblurring. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.3834-3842.
[21] Wieschollek P, Hirsch M, Schölkopf B et al. Learning blind motion deblurring. In Proc. the 2017 IEEE International Conference on Computer Vision, July 2017, pp.231-240.
[22] Yuan L, Sun J, Quan L et al. Image deblurring with blurred/noisy image pairs. ACM Transactions on Graphics, 2007, 26(3):Article No. 1.
[23] Rav-Acha A, Peleg S. Two motion-blurred images are better than one. Pattern Recognition Letters, 2005, 26(3):311- 317.
[24] Shen X, Yan Q, Xu L et al. Multispectral joint image restoration via optimizing a scale map. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(12):2518-2530.
[25] Gehm M E, John R, Brady D J et al. Single-shot compressive spectral imaging with a dual-disperser architecture. Optics Express, 2007, 15(21):14013-14027.
[26] Wu Y, Mirza I O, Arce G R et al. Development of a digital-micromirror-device-based multishot snapshot spectral imaging system. Optics Letters, 2011, 36(14):2692- 2694.
[27] Wang L, Xiong Z, Gao D et al. Dual-camera design for coded aperture snapshot spectral imaging. Applied Optics, 2015, 54(4):848-858.
[28] Takatani T, Aoto T, Mukaigawa Y. One-shot hyperspectral imaging using faced reflectors. In Proc. the 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 2017, pp.2692-2700.
[29] Choi I, Jeon D S, Nam G et al. High-quality hyperspectral reconstruction using a spectral prior. ACM Transactions on Graphics, 2017, 36(6):Article No. 218.
[30] Wang L, Xiong Z, Shi G et al. Adaptive nonlocal sparse representation for dual-camera compressive hyperspectral imaging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(10):2104-2111.
[31] Kim K S, Paik J. Out-of-focus blur estimation and restoration for digital auto-focusing system. Electronics Letters, 1998, 34(12):1217-1219.
[32] Shen C T, Hwang W L, Pei S C. Spatially-varying out-offocus image deblurring with L1-2 optimization and a guided blur map. In Proc. the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, March 2012, pp.1069-1072.
[33] Oliveira J P, Figueiredo M A T, Bioucas-Dias J M. Parametric blur estimation for blind restoration of natural images:Linear motion and out-of-focus. IEEE Transactions on Image Processing, 2014, 23(1):466-477.
[34] Chen S J, Shen H L. Multispectral image out-of-focus deblurring using interchannel correlation. IEEE Transactions on Image Processing, 2015, 24(11):4433-4445.
[35] Shen H L, Zheng Z H, Wang W et al. Autofocus for multispectral camera using focus symmetry. Applied Optics, 2012, 51(14):2616-2623.
[36] Minetomo Y, Kubo H, Funatomi T et al. Acquiring nonparametric scattering phase function from a single image. Computational Visual Media, 2018, 4(4):323-331.
[37] Yasuma F, Mitsunaga T, Iso D et al. Generalized assorted pixel camera:Postcapture control of resolution, dynamic range, and spectrum. IEEE Transactions on Image Processing, 2010, 19(9):2241-2253.
[38] Krishnan D, Tay T, Fergus R. Blind deconvolution using a normalized sparsity measure. In Proc. the 24th IEEE Conference on Computer Vision and Pattern Recognition, June 2011, pp.233-240.
[39] Wald L. Quality of high resolution synthesised images:Is there a simple criterion? In Proc. the 3rd Conference on Fusion of Earth Data:Merging Point Measurements, Raster Maps and Remotely Sensed Images, January 2000, pp.99- 103.
[40] Zhang Y, de Backer S, Scheunders P. Noise-resistant wavelet-based Bayesian fusion of multispectral and hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(11):3834-3843.
[41] Wei Q, Bioucas-Dias J, Dobigeon N et al. Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7):3658-3668.
[42] Golub G H, Heath M, Wahba G. Generalized crossvalidation as a method for choosing a good ridge parameter. Technometrics, 1979, 21(2):215-223.
[1] Ri-Sheng Liu, Cai-Sheng Mao, Zhi-Hui Wang, Hao-Jie Li. Blind Image Deblurring via Adaptive Optimization with Flexible Sparse Structure Control [J]. Journal of Computer Science and Technology, 2019, 34(3): 609-621.
[2] Shao-Lin Chen (陈绍林), Xi-Yuan Hu (胡晰远), Member, IEEE, and Si-Long Peng (彭思龙). Hyperspectral Imagery Denoising Using a Spatial-Spectral Domain Mixing Prior [J]. , 2012, 27(4): 851-861.
Full text



[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .
[10] Li Minghui;. CAD System of Microprogrammed Digital Systems[J]. , 1987, 2(3): 226 -235 .

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
  Copyright ©2015 JCST, All Rights Reserved