Document Type : Original/Review Paper

Authors

1 Faculty of Computer science, Higher Education Complex of Bam, Bam, Iran.

2 Faculty of Computer Engineering, Higher Education Complex of Bam, Bam, Iran.

10.22044/jadm.2020.9309.2068

Abstract

Estimation of blurriness value in image is an important issue in image processing applications such as image deblurring. In this paper,
a no-reference blur metric with low computational cost is proposed, which is based on the difference between the second order
gradients
of a sharp image and the one associated with its blurred version. The experiments, in this paper, performed on four databases,
including CSIQ, TID2008, IVC, and LIVE. The experimental results indicate the capability of the proposed blur metric in measuring
image blurriness, also the low computational cost, comparing with other existing approaches.

Keywords

[1] Antkowiak, J., Jamal Baina, T., Baroncini, F. V., Chateau, N., FranceTelecom, F., Pessoa, A. C. F., Philips, F. (2000). Final report from the video quality experts group on the validation of objective models of video quality assessment march 2000.

[2] Bong, D. B., & Khoo, B. E. (2014). An efficient and training-free blind image blur assessment in the spatial domain. IEICE TRANSACTIONS on Information and Systems, vol. 97, no. 7, pp. 1864–1871.

[3] Bong, D. B. L., & Khoo, B. E. (2014). Blind image blur assessment by using valid reblur range and histogram shape difference. Signal Processing: Image Communication, vol. 29, no. 6, pp. 699–710.

[4] Bromiley, P. (2003). Products and convolutions of gaussian distributions. Medical School, Univ. Manchester, Manchester, UK, Tech. Rep, vol. 3, 2003.

[5] Caviedes, J., & Oberti, F. (2004). A new sharpness metric based on local kurtosis, edge and energy information. Signal Processing: Image Communication, vol. 19, no. 2, pp. 147–161.

[6] Chen, M.-J., & Bovik, A. C. (2011). No-reference image blur assessment using multiscale gradient. EURASIP Journal on image and video processing, 2011(1), 3.

[7] Chern, N. N. K., Neow, P. A., & Ang, M. H. (2001). Practical issues in pixel-based autofocusing for machine vision. In Proceedings 2001 icra. ieee international conference on robotics and automation (Cat. No. 01ch37164), vol. 3, pp. 2791–2796.

[8] Chora S., R. S. (2010). Image processing and communications challenges 7. Springer.

[9] Erasmus, S., & Smith, K. (1982). An automatic focusing and astigmatism correction system for the sem and ctem. Journal of Microscopy, vol. 127, no. 2, pp. 185–199.

[10] Feichtenhofer, C., Fassold, H., & Schallauer, P. (2013). A perceptual image sharpness metric based on local edge gradient analysis. IEEE Signal Processing Letters, vol. 20, no. 4, pp. 379–382.

[11] Ferzli, R., & Karam, L. J. (2009). A no-reference objective image sharpness metric based on the notion of just noticeable blur (jnb). IEEE transactions on image processing, vol. 18, no. 4, pp. 717–728.

[12] Ghosh Roy, G. (2013). A simple second derivative based blur estimation technique (Unpublished doctoral dissertation). The Ohio State University.

[13] Hassen, R., Wang, Z., & Salama, M. M. (2013). Image sharpness assessment based on local phase coherence. IEEE Transactions on Image Processing, vol. 22, no. 7, pp. 2798–2810.

[14] He, L., Zhong, Y., Lu, W., & Gao, X. (2019). A visual residual perception optimized network for blind image quality assessment. IEEE Access, vol. 7, pp. 176087–176098.

[15] Javaran, T. A., Hassanpour, H., & Abolghasemi, V. (2016). A noise-immune no-reference metric for estimating blurriness value of an image [Journal Article]. Signal Processing: Image Communication,

Elsevier, pp. 218-228.

[16] Larson, E. C., & Chandler, D. M. (2010). Most apparent distortion: full-reference image quality assessment and the role of strategy. Journal of Electronic Imaging, vol. 19, no. 1, 011006.

[17] Le Callet, P., & Autrusseau, F. (2005). Subjective quality assessment irccyn/ivc database.

[18] Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., & Kot, A. C. (2015). No-reference image blur assessment based on discrete orthogonal moments. IEEE transactions on cybernetics, vol. 46, no. 1, pp. 39–50.

[19] Li, Q., Lin, W., Gu, K., Zhang, Y., & Fang, Y. (2019). Blind image quality assessment based on joint log-contrast statistics. Neurocomputing, vol. 331, pp. 189–198.

[20] Liu, L., Gong, J., Huang, H., & Sang, Q. (2020). Blind image blur metric based on orientation-aware local patterns. Signal Processing: Image Communication, vol. 80, 115654.

[21] Marichal, X., Ma, W.-Y., & Zhang, H. (n.d.). Blur determination in the compressed domain using dct information [Conference Proceedings]. In Image processing, 1999. icip 99. proceedings. 1999 international conference on (Vol. 2, p. 386-390). IEEE.

[22] Marziliano, P., Dufaux, F., Winkler, S., & Ebrahimi, T. (2002). A no-reference perceptual blur metric. In Proceedings. International conference on image processing (Vol. 3, pp. III–III).

[23] Narvekar, N. D., & Karam, L. J. (2011). A no-reference image blur metric based on the cumulative probability of blur detection (cpbd). IEEE Transactions on Image Processing, vol. 20, no. 9, pp. 2678–2683.

[24] Ong, E., Lin, W., Lu, Z., Yang, X., Yao, S., Pan, F., . . . Moschetti, F. (2003). A no-reference quality metric for measuring image blur. In Seventh international symposium on signal processing and its applications, 2003. proceedings. (Vol. 1, pp. 469–472).

[25] Ponomarenko, N., Lukin, V., Zelensky, A., Egiazarian, K., Carli, M., & Battisti, F. (2009). Tid2008-a database for evaluation of fullreference visual quality assessment metrics. Advances of Modern Radioelectronics, vol. 10, no. 4, pp. 30–45.

[26] Saad, M. A., Bovik, A. C., & Charrier, C. (2012). Blind image quality assessment: A natural scene statistics approach in the dct domain. IEEE transactions on Image Processing, vol. 21, no. 8, pp. 3339–3352.

[27] Shaked, D., & Tastl, I. (2005). Sharpness measure: Towards automatic image enhancement. In Ieee international conference on image processing 2005 (Vol. 1, pp. I–937).

[28] Sheikh, H. (2005). Live image quality assessment database release 2. http://live. ece. utexas. edu/research/quality.

[29] Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006). A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on image processing, vol. 15, no. 11, pp. 3440–3451.

[30] Vu, C. T., Phan, T. D., & Chandler, D. M. (2011). S3: A spectral and spatial measure of local perceived sharpness in natural images. IEEE transactions on image processing, vol. 21, no. 3, pp. 934–945.

[31] Wu, J., Zeng, J., Dong, W., Shi, G., & Lin, W. (2019). Blind image quality assessment with hierarchy: Degradation from local structure to deep semantics. Journal of Visual Communication and Image Representation, vol. 58, pp. 353–362.

 

[32] Xu, S., Jiang, S., & Min, W. (2017). No-reference/blind image quality assessment: a survey. IETE Technical Review, vol. 34, no. 3, pp. 223– 245.

[33] Xu, Y., Zheng, W., Qi, J., & Li, Q. (2019). Blind image blur assessment based on markov-constrained fcm and blur entropy. In 2019 ieee international conference on image processing (icip) (pp. 4519–4523).

[34] Zhang, S., Li, P., Xu, X., Li, L., & Chang, C.-C. (2018). No- reference image blur assessment based on response function of singular values. Symmetry, vol. 10,  no. 8, 304.

[35] Fadaei-Kermani, E., Barani, G., Ghaeini-Hessaroeyeh, M. (2017). Drought Monitoring and Prediction using K-Nearest Neighbor Algorithm. Journal of AI and Data Mining, vol. 5, no. 2, pp. 319-325. doi: 10.22044/jadm.2017.881.

[36] Zhu, X., & Milanfar, P. (2009). A no-reference sharpness metric sensitive to blur and noise. In 2009 international workshop on quality of multimedia experience (pp. 64–69).