Quality Assessment Based Coded Apertures for Defocus Deblurring
Subject Areas : Image ProcessingMina Masoudifar 1 , Hamid Reza Pourreza 2
1 - Ferdowsi University of Mashad
2 - Computer
Keywords: Coded Aperture, Blur, Defocus, Computational Imaging, Image Quality Assessment,
Abstract :
A conventional camera with small size pixels may capture images with defocused blurred regions. Blurring, as a low-pass filter, attenuates or drops details of the captured image. This fact makes deblurring as an ill-posed problem. Coded aperture photography can decrease destructive effects of blurring in defocused images. Hence, in this case, aperture patterns are designed or evaluated based on the manner of reduction of these effects. In this paper, a new function is presented that is applied for evaluating the aperture patterns which are designed for defocus deblurring. The proposed function consists of a weighted sum of two new criteria, which are defined based on spectral characteristics of an aperture pattern. On the basis of these criteria, a pattern whose spectral properties are more similar to a flat all-pass filter is assessed as a better pattern. The weights of these criteria are determined by a learning approach. An aggregate image quality assessment measure, including an existing perceptual metric and an objective metric, is used for determining the weights. According to the proposed evaluation function, a genetic algorithm that converges to a near-optimal binary aperture pattern is developed. In consequence, an asymmetric and a semi-symmetric pattern are proposed. The resulting patterns are compared with the circular aperture and some other patterns in different scenarios.
[1] K. Mitra, O. Cossairt, and A. Veeraraghavan, "To denoise or deblur: parameter optimization for imaging systems," in IS&T/SPIE Electronic Imaging, 2014, pp. 90230G-90230G-6.
#[2] C. Zhou and S. Nayar, "What are good apertures for defocus deblurring?," in Computational Photography (ICCP), 2009 IEEE International Conference on, 2009, pp. 1-8.
#[3] A. Veeraraghavan, R. Raskar, A. Agrawal, A. Mohan, and J. Tumblin, "Dappled photography: Mask enhanced cameras for heterodyned light fields and coded aperture refocusing," ACM Transaction on Graphics, vol. 26, no.3, p. 69, 2007.
#[4] B. Masia, L. Presa, A. Corrales, and D. Gutierrez, "Perceptually optimized coded apertures for defocus deblurring," Computer Graphics Forum, vol. 31, no.6, pp. 1867-1879, 2012.
#[5] A. Levin, R. Fergus, F. Durand, and W. T. Freeman, "Image and depth from a conventional camera with a coded aperture," ACM Transactions on Graphics, vol. 26, no. 3, p. 70, 2007.
#[6] S. Hiura and T. Matsuyama, "Depth measurement by the multi-focus camera," in Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, 1998, pp. 953-959.
#[7] M. Martinello, "Coded aperture imaging," Heriot-Watt University, 2012.
#[8] A. Sellent and P. Favaro, "Which side of the focal plane are you on?," in Computational Photography (ICCP), 2014 IEEE International Conference on, 2014, pp. 1-8.
#[9] A. Sellent and P. Favaro, "Optimized aperture shapes for depth estimation," Pattern Recognition Letters, vol. 40, pp. 96-103, 2014.
#[10] Y. Bando, B.-Y. Chen, and T. Nishita, "Extracting depth and matte using a color-filtered aperture," ACM Transactions on Graphics, vol. 27, no.5, p. 134., 2008.
#[11] C. Zhou, S. Lin, and S. K. Nayar, "Coded aperture pairs for depth from defocus and defocus deblurring," International Journal of Computer Vision, vol. 93, no. 1, pp. 53-72, 2011.
#[12] Y. Takeda, S. Hiura, and K. Sato, "Fusing depth from defocus and stereo with coded apertures," in Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, 2013, pp. 209-216.
#[13] A. Chakrabarti and T. Zickler, "Depth and deblurring from a spectrally-varying depth-of-field," in Computer Vision–ECCV 2012, ed: Springer, 2012, pp. 648-661.
#[14] A. Ashok and M. A. Neifeld, "Pseudorandom phase masks for superresolution imaging from subpixel shifting," Applied optics, vol. 46, no. 12, pp. 2256-2268, 2007.
#[15] S. K. Nayar, "Computational cameras: Approaches, benefits and limits," Technical Rep. 2011.
#[16] C. Zhou and S. K. Nayar, "Computational cameras: Convergence of optics and processing," Image Processing, IEEE Transactions on, vol. 20, no. 12, pp. 3322-3340, 2011.
#[17] E. Caroli, J. Stephen, G. Di Cocco, L. Natalucci, and A. Spizzichino, "Coded aperture imaging in X-and gamma-ray astronomy," Space Science Reviews, vol. 45, no.3, pp. 349-403, 1987.
#[18] S. R. Gottesman and E. Fenimore, "New family of binary arrays for coded aperture imaging," Applied optics, vol. 28, no. 20, pp. 4344-4352, 1989.
#[19] W. T. Welford, "Use of annular apertures to increase focal depth," Journal of the Optical Society of America, vol. 50, no. 8, pp. 749-752, 1960.
#[20] M. Mino and Y. Okano, "Improvement in the OTF of a defocused optical system through the use of shaded apertures," Applied Optics, vol. 10, no. 10, pp. 2219-2225, 1971.
#[21] P. C. Hansen, J. G. Nagy, and D. P. O'leary, Deblurring images: matrices, spectra, and filtering: Siam, 2006.
#[22] P. Campisi and K. Egiazarian, Blind image deconvolution: theory and applications: CRC press, 2007.
#[23] H. R. Sheikh and A. C. Bovik, "Image information and visual quality," Image Processing, IEEE Transactions on, vol. 15, no. 2, pp. 430-444, 2006.
#[24] H. R. Sheikh, M. F. Sabir, and A. C. Bovik, "A statistical evaluation of recent full reference image quality assessment algorithms," Image Processing, IEEE Transactions on, vol. 15, no. 11, pp. 3440-3451, 2006.
#[25] A. Lahoulou, A. Bouridane, E. Viennet, and M. Haddadi, "Full-reference image quality metrics performance evaluation over image quality databases," Arabian Journal for Science and Engineering, vol. 38, no. 9, pp. 2327-2356, 2013.
[26] Y. Liu, J. Wang, S. Cho, A. Finkelstein, and S. Rusinkiewicz, "A no-reference metric for evaluating the quality of motion deblurring," ACM Transaction on Graphics, vol. 32, no. 6, p. 175, 2013.
[27] O. Cossairt, M. Gupta, and S. K. Nayar, "When does computational imaging improve performance?," Image Processing, IEEE Transactions on, vol. 22, no. 2, pp. 447-458, 2013.
[28] K. Mitra, O. S. Cossairt, and A. Veeraraghavan, "A Framework for Analysis of Computational Imaging Systems: Role of Signal Prior, Sensor Noise and Multiplexing," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 36, no. 10, pp. 1909-1921, 2014.
[29] Y. Weiss and W. T. Freeman, "What makes a good model of natural images?," in Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on, 2007, pp. 1-8.
[30] Y. Gao, "Population size and sampling complexity in genetic algorithms," in Proc. of the Bird of a Feather Workshops, 2003, pp. 178-181.
[31] http://www.cs.washington.edu/research/imagedatabase/groundtruth/
