Abstract
This paper presents a novel iterative feedback framework for simultaneous estimation of depth map and All-In-Focus (AIF) image, which benefits each other in each stage to obtain final convergence: For the recovery of AIF image, sparse prior of natural image is incorporated to ensure high quality defocus removal even under inaccurate depth estimation. In depth estimation step, we feed back the constraints from the high quality AIF image and adopt a numerical solution which is robust to the inaccuracy of AIF recovery to further raise the performance of DFD algorithm. Compared with traditional DFD methods, another advantage offered by this iterative framework is that by introducing AIF, which follows the prior knowledge of natural images to regularize the depth map estimation, DFD is much more robust to camera parameter changes. In addition, the proposed approach is a general framework that can incorporate depth estimation and AIF image recovery algorithms. The experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method, especially on the challenging data sets containing large textureless regions and within a large range of camera parameters.
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References
Schechner, Y.Y., Kiryati, N.: Depth from defocus vs.stereo: How different really are they? International Journal of Computer Vision 39, 141–162 (2000)
Krotkov, E.: Focusing. International Journal of Computer Vision 1, 223–237 (1987)
Nayar, S.K., Nakagawa, Y.: Shape from focus. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 824–831 (1994)
Shoji, H., Shirai, K., Ikehara, M.: Shape from focus using color segmentation and bilateral filter. In: Proceedings of 4th Signal Processing Education Workshop, pp. 566–571 (2006)
Chaudhuri, S., Rajagopalan, A.N.: Depth from defocus: a real aperture imaging approach. Springer (1999)
Favaro, P., Soatto, S.: 3D shape reconstruction and image restoration: exploiting defocus and motion blur. Springer (2006)
Levin, A., Fergus, R., Durand, F., Freeman, W.: Image and depth from a conventional camera with a coded aperture. ACM Transactions on Graphics (Proc. SIGGRAPH) 26, 70–78 (2007)
Zhuo, S., Sim, T.: Recovering depth from a single defocused image (submitted to Pattern Recognition and online available)
Pentland, A.: A new sense for depth of field. IEEE Transactions on Pattern Analysis and Machine Intelligence 9, 523–531 (1987)
Subbarao, M., Surya, G.: Depth from defocus: A spatial domain approach. International Journal of Computer Vision 13, 271–294 (1994)
Favaro, P., Soatto, S.: Learning Shape from Defocus. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 735–745. Springer, Heidelberg (2002)
Subbarao, M.: Parallel depth recovery by changing camera aperture. In: IEEE International Conference on Computer Vision, pp. 149–155 (1988)
Gokstorp, M.: Computing depth from out-of-focus blur using a local frequency representation. In: International Conference on Pattern Recognition, pp. 153–158 (1994)
Rajagopalan, A., Chaudhuri, S.: A block shift-variant blur model for recovering depth from defocused images. In: International Conference on Image Processing, pp. 636–639 (1995)
Watanabe, M., Nayar, S.: Rational filters for passive depth from defocus. International Journal of Computer Vision 27, 203–225 (1998)
Rajagopalan, A., Chaudhuri, S.: An MRF model-based approach to simultaneous recovery of depth and restoration from defocused images. IEEE Transactions on Pattern Analysis and Machine Intelligence 21, 577 (1999)
Namboodiri, V.P., Chaudhuri, S., Hadap, S.: Regularized depth from defocus. In: IEEE International Conference on Image Processing, pp. 1520–1523 (2008)
Favaro, P., Mennucci, A., Soatto, S.: Observing shape from defocused images. International Journal of Computer Vision 52, 25–43 (2003)
Rajagopalan, A., Chaudhuri, S.: Space-variant approaches to recovery of depth from defocused images. Computer Vision and Image Understanding 68, 309–329 (1997)
Favaro, P.: Recovering thin structures via nonlocal-means regularization with application to depth from defocus. In: International Conference on Computer Vision and Pattern Recognition, pp. 1133–1140 (2010)
Favaro, P., Soatto, S., Burger, M., Osher, S.: Shape from defocus via diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 518–531 (2008)
Namboodiri, V., Chaudhuri, S.: On defocus, diffusion and depth estimation. Pattern Recognition Letters 28, 311–319 (2007)
Cai, J., Ji, H., Liu, C., Shen, Z.: High-quality curvelet based motion deblurring from an image pair. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1566–1573 (2009)
Chen, J., Yuan, L., Tang, C., Quan, L.: Robust dual motion deblurring. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Rav-Acha, A., Peleg, S.: Two motion-based images are better than one. Pattern Recognition Letters 26, 311–317 (2005)
Tao, M., Yang, J.: Alternating direction algorithms for total variation deconvolution in image reconstruction. available at Optimization Online (2009)
Zitnick, C., Kang, S., Uyttendaele, M., Winder, S., Szeliski, R.: High-quality video view interpolation using a layered representation. ACM Transactions on Graphics (also Proc. SIGGRAPH) 23, 600–608 (2004)
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Lin, X., Suo, J., Cao, X., Dai, Q. (2013). Iterative Feedback Estimation of Depth and Radiance from Defocused Images. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37447-0_8
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DOI: https://doi.org/10.1007/978-3-642-37447-0_8
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