Abstract
Most image restoration techniques build “universal” image priors, trained on a variety of scenes, which can guide the restoration of any image. But what if we have more specific training examples, e.g. sharp images of similar scenes? Surprisingly, state-of-the-art image priors don’t seem to benefit from from context-specific training examples. Re-training generic image priors using ideal sharp example images provides minimal improvement in non-blind deconvolution. To help understand this phenomenon we explore non-blind deblurring performance over a broad spectrum of training image scenarios. We discover two strategies that become beneficial as example images become more context-appropriate: (1) locally adapted priors trained from region level correspondence significantly outperform globally trained priors, and (2) a novel multi-scale patch-pyramid formulation is more successful at transferring mid and high frequency details from example scenes. Combining these two key strategies we can qualitatively and quantitatively outperform leading generic non-blind deconvolution methods when context-appropriate example images are available. We also compare to recent work which, like ours, tries to make use of context-specific examples.
Chapter PDF
Similar content being viewed by others
References
Cho, S., Lee, S.: Fast motion deblurring. ACM Transactions on Graphics (2009)
Cho, T.S., Joshi, N., Zitnick, C.L., Kang, S.B., Szeliski, R., Freeman, W.T.: A content-aware image prior. In: CVPR (2010)
Cho, T.S., Zitnick, C.L., Joshi, N., Kang, S.B., Szeliski, R., Freeman, W.T.: Image restoration by matching gradient distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Transactions on Graphics (2006)
HaCohen, Y., Shechtman, E., Goldman, D., Lischinski, D.: Non-rigid dense correspondence with applications for image enhancement. ACM Transactions on Graphics (2011)
HaCohen, Y., Shechtman, E., Lischinski, D.: Deblurring by example using dense correspondence. In: ICCV (2013)
Hays, J., Efros, A.A.: Im2gps: estimating geographic information from a single image. In: CVPR (2008)
Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., Harmeling, S.: Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 27–40. Springer, Heidelberg (2012)
Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. In: NIPS (2009)
Levi, E.: Using Natural Image Priors: Maximizing Or Sampling? Hebrew University of Jerusalem (2009), http://leibniz.cs.huji.ac.il/tr/1207.pdf
Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics (2007)
Levin, A., Nadler, B., Durand, F., Freeman, W.T.: Patch complexity, finite pixel correlations and optimal denoising. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 73–86. Springer, Heidelberg (2012)
Levin, A., Weiss, Y.: User assisted separation of reflections from a single image using a sparsity prior. TPAMI (2007)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR (2009)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: CVPR (2011)
Roth, S., Black, M.J.: Fields of experts: A framework for learning image priors. In: CVPR (2005)
Schmidt, U., Rother, C., Nowozin, S., Jancsary, J., Roth, S.: Discriminative non-blind deblurring. In: CVPR (2013)
Schuler, C., Burger, H., Harmeling, S., Schölkopf, B.: A machine learning approach for non-blind image deconvolution. In: CVPR (2013)
Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based blur kernel estimation using patch priors. In: ICCP (2013)
Weiss, Y., Freeman, W.T.: What makes a good model of natural images? In: CVPR (2007)
Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010)
Yu, G., Sapiro, G., Mallat, S.: Solving inverse problems with piecewise linear estimators: From gaussian mixture models to structured sparsity. Transactions on Image Processing (2012)
Yue, H., Sun, X., Yang, J., Wu, F.: Landmark image super-resolution by retrieving web images. IEEE Transactions on Image Processing (2013)
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: ICCV (2011)
Zuo, W., Zhang, L., Song, C., Zhang, D.: Texture enhanced image denoising via gradient histogram preservation. In: CVPR (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Sun, L., Cho, S., Wang, J., Hays, J. (2014). Good Image Priors for Non-blind Deconvolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8692. Springer, Cham. https://doi.org/10.1007/978-3-319-10593-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-10593-2_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10592-5
Online ISBN: 978-3-319-10593-2
eBook Packages: Computer ScienceComputer Science (R0)