Computational All-in-Focus Imaging using an Optical Phase Mask

A method for extended depth of field imaging based on image acquisition through a thin binary phase plate followed by fast automatic computational post-processing is presented. By placing a wavelength dependent optical mask inside the pupil of a conventional camera lens, one acquires a unique response for each of the three main color channels, which adds valuable information that allows blind reconstruction of blurred images without the need of an iterative search process for estimating the blurring kernel. The presented simulation as well as capture of a real life scene show how acquiring a one-shot image focused at a single plane, enable generating a de-blurred scene over an extended range in space. ©2015 Optical Society of America OCIS codes: (110.1758) Computational imaging; (110.1455) Blind deconvolution; (110.3010) Image reconstruction techniques; (100.3020) Image reconstruction-restoration; (100.3190) Inverse problems References and links 1. B. Milgrom, N. Konforti, M. A. Golub, and E. Marom, “Pupil coding masks for imaging polychromatic scenes with high resolution and extended depth of field,” Opt Express, 18 (15), 15569–15584, (2010). 2. B. Milgrom, N. Konforti, M. A. Golub, and E. Marom, “Novel approach for extending the depth of field of Barcode decoders by using RGB channels of information,” Opt Express, 18 (16), 17027–17039, (2010). 3. J. L. Starck, E. Pantin, and F. Murtagh, “Deconvolution in Astronomy: A Review,” Publ. Astron. Soc. Pacific, The University of Chicago Press, 114 (800), 1051–1069, (2002). 4. M. Elad, Sparse and redundant representations : from theory to applications in signal and image processing. (Springer, 2010). 5. M. Elad and M. Aharon, “Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries,” IEEE Trans. Image Process., 15 (12), 3736–3745, (2006). 6. M. J. Fadili, J. L. Starck, and F. Murtagh, “Inpainting and zooming using sparse representations,” Comput. J., 52 (1), 64–79, (2009). 7. F. Couzinie-Devy, J. Mairal, F. Bach, and J. Ponce, “Dictionary learning for deblurring and digital zoom,” arXiv Prepr. arXiv1110.0957, (2011). 8. M. S. C. Almeida and L. B. Almeida, “Blind and semi-blind deblurring of natural images.,” IEEE Trans. Image Process., 19 (1), 36–52, (2010). 9. Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image,” in ACM Transactions on Graphics (TOG), ACM, 27 (3), pp. 73. 10. Z. Hu, J. Bin Huang, and M. H. Yang, “Single image deblurring with adaptive dictionary learning,” in Image Processing (ICIP), 2010 17th IEEE International Conference on, IEEE, pp. 1169–1172. 11. D. Krishnan, T. Tay, and R. Fergus, “Blind deconvolution using a normalized sparsity measure,” in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, IEEE, pp. 233–240. 12. R. Ng, M. Levoy, M. Brédif, G. Duval, M. Horowitz, and P. Hanrahan, “Light field photography with a handheld plenoptic camera,” Comput. Sci. Tech. Rep. CSTR, 2 (11), (2005). 13. A. Levin, R. Fergus, F. Durand, and W. T. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graph., 26 (3), 70, (2007). 14. F. Guichard, H.-P. Nguyen, R. Tessières, M. Pyanet, I. Tarchouna, and F. Cao, “Extended depth-of-field using sharpness transport across color channels,” in IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics (2009), pp. 72500N–72500N–12. 15. J. W. Goodman, Introduction to Fourier optics, 2nd ed. (McGraw-Hill, 1996).