Abstract
In this paper, a hybrid defocused region segmentation using image matting is proposed. The technique incorporates three sharpness metrics which are magnitude spectrum slope, local total variation and local binary patterns to identify the in-focus pixels in the image. Trimap is generated automatically using sharpness maps to obtain the prior information and matting Laplacian is applied to propagate the trimap to the entire image based on color similarities. Simulation results compared using visual and quantitative metrics show the strength of the proposed technique.
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Bae, S., & Durand, F. (2007). Defocus magnification. Computer Graphics Forum, 26, 571–579.
Bahrami, K., Kot, A. C., Fan, J. (2013). A novel approach for partial blur detection and segmentation. In IEEE International Conference on Multimedia and Expo, pp. 1–6.
Chakrabarti, A., Zickler, T., & Freeman, W. T. (2010). Analyzing spatially varying blur. In IEEE International Conference on Computer Vision and Pattern Recognition, pp. 2512–2519.
Chen, D. J., Chen, H. T., & Chang, L. W. (2016). Fast defocus map estimation. In International Conference on Image Processing, pp. 3091–3966.
Couzinie-Devy, F., Sun, J., Alahari, K., & Ponce, J. (2013). Learning to estimate and remove non-uniform image blur. In IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1075–1082.
Field, D. J., & Brady, N. (1997). Visual sensitivity, blur and the sources of variability in the amplitude spectra of natural scenes. Vision Research, 37(23), 3367–3383.
Levin, A., Lischinski, D., & Weiss, Y. (2008). A closed-form solution to natural image matting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(2), 228–242.
Liu, R., Li, Z., & Jia, J. (2008). Image partial blur detection and classification. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8.
Ma, K., Fu, H., Liu, T., Wang, Z., & Tao, D. (2016). Local blur mapping: Exploiting high-level semantics by deep neural networks. In IEEE Conference on Computer Vision and Pattern Recognition.
Shi, J., Xu, L., & Jia, J. (2014a). Blur detection dataset. http://www.cse.cuhk.edu.hk/~leojia/projects/dblurdetect/dataset.html.
Shi, J., Xu, L., & Jia, J. (2014b). Discriminative blur detection features. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 2965–2972.
Shi, J., Xu, L., & Jia, J. (2015). Just noticeable defocus blur detection and estimation. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 657–665.
Su, B., Lu, S., & Tan, C. L. (2011). Blurred image region detection and classification. In ACM International Conference on Multimedia, pp. 1397–1400.
Tang, C., Wu, J., Hou, Y., Wang, P., & Li, W. (2016). A spectral and spatial approach of coarse-to-fine blurred image region detection. IEEE Signal Processing Letters, 23(11), 1652–1656.
Thongkamwitoon, T., Muammar, H., & Dragotti, P.-L. (2015). An image recapture detection algorithm based on learning dictionaries of edge profiles. IEEE Transactions on Information Forensics and Security, 10(5), 953–968.
Vu, C. T., Phan, T. D., & Chandler, D. M. (2012). S3: A spectral and spatial measure of local perceived sharpness in natural images. IET Image Processing, 21(3), 934–945.
Wang, J., & Cohen, M. (2005). An iterative optimization approach for unified image segmentation and matting. In International Conference on Computer Vision. pp. 17–21.
Yang, D., & Qin, S. (2016). Restoration of partial blurred image based on blur detection and classification. Journal of Electrical and Computer Engineering, 2016(1), 1–12.
Yi, X., & Eramian, M. (2016). LBP-based segmentation of defocus blur. IEEE Transactions on Image Processing, 25(4), 1626–1638.
Zhang, X., Wang, R., Jiang, X., Wang, W., & Gao, W. (2016a). Spatially variant defocus blur map estimation and deblurring from a single image. Journal of Visual Communication and Image Representation, 35, 257–264.
Zhang, L., & Zhang, D. (2016a). Robust visual knowledge transfer via extreme learning machine-based domain adaptation. IEEE Transactions on Image Processing, 25(10), 4959–4973.
Zhang, L., & Zhang, D. (2016b). Evolutionary cost-sensitive extreme learning machine. IEEE Transactions on Neural Networks and Learning Systems, PP(99), 1–6.
Zhang, L., & Zhang, D. (2016c). Visual understanding via multi-feature shared learning with global consistency. IEEE Transactions on Multimedia, 18(2), 247–259.
Zhang, L., Zuo, W., & Zhang, D. (2016b). LSDT: Latent sparse domain transfer learning for visual adaptation. IEEE Transactions on Image Processing, 25(3), 1177–1191.
Zhao, J., Feng, H., Xu, Z., Li, Q., & Tao, X. (2013). Automatic blur region segmentation approach using image matting. Signal, Image and Video Processing, 7(6), 1173–1181.
Zhu, X., Cohen, S., Schiller, S., & Milanfar, P. (2013). Estimating spatially varying defocus blur from a single image. IEEE Transactions on Image Processing, 22(12), 4879–4891.
Zhuo, S., & Sim, T. (2011). Defocus map estimation from a single image. Pattern Recognition, 44(9), 1852–1858.
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Amin, B., Riaz, M.M. & Ghafoor, A. A hybrid defocused region segmentation approach using image matting. Multidim Syst Sign Process 30, 561–569 (2019). https://doi.org/10.1007/s11045-018-0570-8
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DOI: https://doi.org/10.1007/s11045-018-0570-8