利用低秩先验的噪声模糊图像盲去卷积

孙士洁; 赵怀慈; 李波; 郝明国; 吕进锋

doi:10.11999/JEIT161206

利用低秩先验的噪声模糊图像盲去卷积

doi: 10.11999/JEIT161206

基金项目:

辽宁省教育厅科研项目(L2015368)

计量
- 文章访问数: 1341
- HTML全文浏览量: 224
- PDF下载量: 364
- 被引次数: 0
出版历程
- 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: 单幅图像盲去卷积的目的是从一幅观测的模糊图像估计出模糊核和清晰图像。该问题是严重病态的，尤其是观测图像中噪声不可忽略时更具挑战性。该文主要针对如何有效利用低秩先验约束进行噪声模糊图像盲去卷积问题，提出一种在交替最大后验(MAP)估计框架下利用低秩先验约束的单幅噪声模糊图像盲去卷积方法。首先，在估计中间复原图像时，利用低秩先验约束对复原图像中的噪声进行抑制。然后，采用降噪后的中间复原图像估计模糊核，得到更好质量的模糊核估计。迭代上述两个操作获得最终可靠的模糊核估计。最后，根据所估计的模糊核，通过非盲去卷积方法复原出清晰图像。实验结果表明：所提方法在定量和定性评价指标上优于已有的代表性方法。
- 盲去卷积 /
- 最大后验估计 /
- 噪声模糊图像 /
- 低秩先验
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

HTML全文

参考文献(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.

施引文献

资源附件(0)

访问统计