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Multichannel deblurring of digital images
Kybernetika, Tome 47 (2011) no. 3, pp. 439-454 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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Blur is a common problem that limits the effective resolution of many imaging systems. In this article, we give a general overview of methods that can be used to reduce the blur. This includes the classical multi-channel deconvolution problems as well as challenging extensions to spatially varying blur. The proposed methods are formulated as energy minimization problems with specific regularization terms on images and blurs. Experiments on real data illustrate very good and stable performance of the methods.
Blur is a common problem that limits the effective resolution of many imaging systems. In this article, we give a general overview of methods that can be used to reduce the blur. This includes the classical multi-channel deconvolution problems as well as challenging extensions to spatially varying blur. The proposed methods are formulated as energy minimization problems with specific regularization terms on images and blurs. Experiments on real data illustrate very good and stable performance of the methods.
Classification : 15A29, 92C55
Keywords: image restoration; blind deconvolution; deblurring; spatially varying blur 
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     title = {Multichannel deblurring of digital images},
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Šorel, Michal; Šroubek, Filip; Flusser, Jan. Multichannel deblurring of digital images. Kybernetika, Tome 47 (2011) no. 3, pp. 439-454. http://geodesic.mathdoc.fr/item/KYB_2011_47_3_a8/

[1] Ahmed, M., Farag, A.: Non-metric calibration of camera lens distortion. In: Proc. Internat. Conf. of Image Processing 2001, Vol. 2, pp. 157–160.

[2] Banham, M. R., Katsaggelos, A. K.: Digital image restoration. IEEE Signal Process. Mag. 14 (1997), 2, 24–41. | DOI

[3] Bar, L., Sochen, N. A., Kiryati, N.: Restoration of images with piecewise space-variant blur. In: SSVM 2007, pp. 533–544.

[4] Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. Trans. Image Process. 18 (2009), 11, 2419–2434. | DOI | MR

[5] Ben-Ezra, M., Lin, Z., Wilburn, B.: Penrose pixels super-resolution in the detector layout domain. In: Proc. IEEE Internat. Conf. Computer Vision 2007, pp. 1–8.

[6] Favaro, P., Burger, M., Soatto, S.: Scene and motion reconstruction from defocus and motion-blurred images via anisothropic diffusion. In: ECCV 2004 (T. Pajdla and J. Matas, eds.), Lecture Notes in Comput. Sci. 3021, Springer Verlag, Berlin – Heidelberg 2004, pp. 257–269.

[7] Favaro, P., Soatto, S.: A variational approach to scene reconstruction and image segmentation from motion-blur cues. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition 2004, Vol. 1, pp. 631–637.

[8] Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., Freeman, W. T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25 (2006), 3, 787–794. | DOI

[9] Heeger, D. J., Jepson, A. D.: Subspace methods for recovering rigid motion. Internat. J. Computer Vision 7 (1992), 2, 95–117. | DOI

[10] Levin, A.: Blind motion deblurring using image statistics. In: NIPS 2006, pp. 841–848.

[11] Levin, A., Fergus, R., Durand, F., Freeman, W. T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26 (2007), 3, 70. | DOI

[12] Levin, A., Sand, P., Cho, T. S., Durand, F., Freeman, W. T.: Motion-invariant photography. In: SIGGRAPH ’08: ACM SIGGRAPH 2008 Papers, ACM, New York 2008, pp. 1–9.

[13] Lim, S. H., Silverstein, A. D.: Method for Deblurring an Image. US Patent Application, Pub. No. US2006/0187308 A1, 2006.

[14] Nagy, J. G., O’Leary, D. P.: Restoring images degraded by spatially variant blur. SIAM J. Sci. Comput. 19 (1998), 4, 1063–1082. | DOI | MR

[15] Rajagopalan, A. N., Chaudhuri, S.: An MRF model-based approach to simultaneous recovery of depth and restoration from defocused images. IEEE Trans. Pattern Anal. Mach. Intell. 21 (1999), 7, 577–589. | DOI

[16] Rudin, L. I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60 (1992), 259–268. | DOI | Zbl

[17] Šroubek, F., Flusser, J.: Multichannel blind iterative image restoration. IEEE Trans. Image Process. 12 (2003), 9, 1094–1106. | DOI | MR

[18] Šroubek, F., Flusser, J.: Multichannel blind deconvolution of spatially misaligned images. IEEE Trans. Image Process. 14 (2005), 7, 874–883. | DOI | MR

[19] Tico, M., Trimeche, M., Vehvilainen, M.: Motion blur identification based on differently exposed images. In: Proc. IEEE Internat. Conf. Image Processing 2006, pp. 2021–2024.

[20] Tschumperlé, D., Deriche, R.: Vector-valued image regularization with pdes: A common framework for different applications. IEEE Trans. Pattern Analysis Machine Intelligence 27(2005), 4, 506–517. | DOI

[21] Šorel, M.: Multichannel Blind Restoration of Images with Space-Variant Degradations. PhD Thesis, Charles University, Prague 2007.

[22] Šorel, M., Flusser, J.: Space-variant restoration of images degraded by camera motion blur. IEEE Trans. Image Process. 17 (2008), 2, 105–116. | DOI | MR

[23] Šorel, M., Šroubek, F.: Space-variant deblurring using one blurred and one underexposed image. In: Proc. Internat. Conf. Image Processing, 2009, pp. 157–160.

[24] M. , Šroubek, F., Flusser, J.: Towards superresolution in the presence of spatially varying blur. In: Super-Resolution Imaging (P. Milanfar, eds.), CRC Press 2010.

[25] Šroubek, F., Cristobal, G., Flusser, J.: A unified approach to superresolution and multichannel blind deconvolution. IEEE Trans. Image Process. 16 (2007), 2322–2332. | DOI | MR

[26] Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition 2010.

[27] Yu, W.: An embedded camera lens distortion correction method for mobile computing applications. IEEE Trans. Consumer Electronics 49 (2003), 4, 894–901. | DOI

[28] Yuan, L., Sun, J., Quan, L., Shum, H.-Y.: Image deblurring with blurred/noisy image pairs. In: SIGGRAPH ’07: ACM SIGGRAPH 2007 Papers, ACM, New York 2007, p. 1.

[29] Zitová, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 11 (2003), 21, 977–1000. | DOI