利用低秩先驗的噪聲模糊圖像盲去卷積

孫士潔; 趙懷慈; 李波; 郝明國; 呂進鋒

doi:10.11999/JEIT161206

利用低秩先驗的噪聲模糊圖像盲去卷積

doi: 10.11999/JEIT161206

基金項目:

遼寧省教育廳科研項目(L2015368)

計量
- 文章訪問數(shù): 1457
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-11-08
- 修回日期: 2017-04-01
- 刊出日期: 2017-08-19

Blind Deconvolution for Noisy and Blurry Images Using Low Rank Prior

Funds:

The Scientific Research Project of the Education Department of Liaoning Province (L2015368)

摘要

摘要: 單幅圖像盲去卷積的目的是從一幅觀測的模糊圖像估計出模糊核和清晰圖像。該問題是嚴重病態(tài)的，尤其是觀測圖像中噪聲不可忽略時更具挑戰(zhàn)性。該文主要針對如何有效利用低秩先驗約束進行噪聲模糊圖像盲去卷積問題，提出一種在交替最大后驗(MAP)估計框架下利用低秩先驗約束的單幅噪聲模糊圖像盲去卷積方法。首先，在估計中間復原圖像時，利用低秩先驗約束對復原圖像中的噪聲進行抑制。然后，采用降噪后的中間復原圖像估計模糊核，得到更好質(zhì)量的模糊核估計。迭代上述兩個操作獲得最終可靠的模糊核估計。最后，根據(jù)所估計的模糊核，通過非盲去卷積方法復原出清晰圖像。實驗結(jié)果表明：所提方法在定量和定性評價指標上優(yōu)于已有的代表性方法。
- 盲去卷積 /
- 最大后驗估計 /
- 噪聲模糊圖像 /
- 低秩先驗
Abstract: The purpose of single image blind deconvolution is to estimate the unknown blur kernel from a single observed blurred image and recover the original sharp image. Such a task is severely ill-posed and even more challenging especially in the condition that the noise in the input image can not be negligible. In this paper, the main problem this study focuses on is how to effectively apply low rank prior to blind deconvolution. A single noisy and blurry image blind deconvolution algorithm is proposed, using alternating Maximum A Posteriori (MAP) estimation combined with low rank prior. First, when estimating the intermediate latent image, low rank prior is used as the constraint that is used for noise suppression of the restored image. Then the denoised intermediate latent image in turn leads to higher quality blur kernel estimation. These two operations are iterated in this manner to arrive at reliable blur kernel estimation. Finally, the non-blind deconvolution method is chosen to be used with sparse prior knowledge to achieve the final latent image restoration. Extensive experiments manifest the superiority of the proposed method over state-of-the-art techniques, both qualitatively and quantitatively.
- Blind deconvolution /
- Maximum A Posteriori (MAP) estimation /
- Noisy and blurry image /
- Low rank prior

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參考文獻(22)

LEVIN A, WEISS Y, DURAND F, et al. Efficient marginal likelihood optimization in blind deconvolution[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2011: 2657-2664. doi: 10.1109/CVPR. 2011.5995308.

KRISHNAN D, TAY T, and FERGUS R. Blind deconvolution using a normalized sparsity measure[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2011: 233-240. doi: 10.1109/CVPR. 2011.5995308.

SUN L, Cho S, Wang J, et al. Edge-based blur kernel estimation using patch priors[C]. IEEE Conference on Computational Photography, Cambridge, MA, USA, 2013: 1-8, 19-21. doi: 10.1109/ICCPhot.2013.6528301.

LAI W S, DING J J, LIN Y Y, et al. Blur kernel estimation using normalized color-line priors[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 64-72. doi: 10.1109/CVPR.2015.7298601.

REN W, CAO X, PAN J, et al. Image deblurring via enhanced low-rank prior[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3426-3437. doi: 10.1109/TIP.2016. 2571062.

CHO S and LEE S. Fast motion deblurring[J]. ACM Transactions on Graphics, 2009, 28(5): 89-97. doi: 10.1145/ 1618452.1618491.

XU L and JIA J Y. Two-phase kernel estimation for robust motion deblurring[C]. European Conference on Computer Vision, Crete, Greece, 2010: 157-170. doi: 10.1007/978-3-642- 15549-9_12.

PAN J, LIU R, SU Z, et al. Kernel estimation from salient structure for robust motion deblurring[J]. Signal Processing: Image Commmunication, 2013, 28(9): 1156-1170. doi: 10. 1016/j.image.2013.05.001.

PAN J, HU Z, SU Z, et al. L0-regularized intensity and gradient prior for text images deblurring and beyond[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(2): 342-355. doi: 10.1109/TPAMI.2016. 2551244.

PAN J, SUN D, PFISTER H, et al. Blind image deblurring using dark channel prior[C]. IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, 2016: 1628-1636. doi: 10.1109/CVPR.2016.180.

MAI L and LIU F. Kernel fusion for better image deblurring[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 371-380. doi: 10.1109/CVPR.2015.7298634.

YAN R M and SHAO L. Blind image blur estimation via deep learning[J]. IEEE Transactions on Image Processing, 2016, 25(4): 1910-1921. doi: 10.1109/TIP.2016.2535273.

TAI Y and LIN S. Motion-aware noise filtering for deblurring of noisy and blurry images[C]. IEEE Conference on Computer Vision and Pattern Recognition, Rhode Island, USA, 2012: 17-24. doi: 10.1109/CVPR.2012.6247653.

ZHONG L, CHO S, METAXAS D, et al. Handling noise in single image deblurring using directional filters[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, 2013: 612-619. doi: 10.1109/CVPR. 2013.85.

WHYTE O, SIVIC J, ZISSERMAN A, et al. Non-uniform deblurring for shaken images[C]. IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010: 491-498. doi: 10.1109/CVPR.2010.5540175.

GU S, ZHANG L, ZUO W, et al. Weighted nuclear norm minimization with application to image denoising[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 2862-2869. doi: 10.1109/CVPR. 2014.366.

DONG W S, SHI G M, and LI X. Nonlocal image restoration with bilateral variance estimation: A low-rank approach[J]. IEEE Transactions on Image Processing, 2013, 22(2): 700-711. doi: 10.1109/TIP.2012.2221729.

CAI J F, CAND?S E J, and SHEN Z. A singular value thresholding algorithm for matrix completion[J]. SIAM Journal on Optimization, 2008, 20(4): 1956-1982. doi: 10. 1137/080738970.

NOCEDAL J and WRIGHT S. Numerical Optimization[M]. NY, USA, Springer-Verlag New York, Inc., 2006: 497-506. doi: 10.1007/978-0-387-40065-5.

LEVIN A, WEISS Y, DURAND F, et al. Understanding and evaluating blind deconvolution algorithms[C]. IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, FL, USA, 2009: 1964-1971. doi: 10.1109/ CVPR.2009.5206815.

WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. doi: 10.1109/TIP.2003.819861.

XU L, ZHENG S, and JIA J Y. Unnatural L0 sparse representation for natural image deblurring[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, 2013: 1107-1114. doi: 10.1109/ CVPR.2013.147.

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