The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
A Fast Wavelet-Based Algorithm for Global and Local Image Sharpness Estimation
Abstract
In this letter, we present a simple, yet effective wavelet-based algorithm for estimating both global and local image sharpness (FISH, Fast Image Sharpness). FISH operates by first decomposing the input image via a three-level separable discrete wavelet transform (DWT). Next, the log-energies of the DWT subbands are computed. Finally, a scalar index corresponding to the image's overall sharpness is computed via a weighted average of these log-energies. Testing on several image databases demonstrates that, despite its simplicity, FISH is competitive with the currently best-performing techniques both for sharpness estimation and for no-reference image quality assessment.
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Cites
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Most apparent distortion: full-reference image quality assessment and the role of strategy
Larson, E. C., & Chandler, D. M. (2010) · Journal of Electronic Imaging · doi:10.1117/1.3267105 · Cited by 1,953
Self-citationLive image quality assessment database release 2
Sheikh, H. R. (2005) · Medical Entomology and Zoology · Cited by 1,275
TID2008 – A database for evaluation of full-reference visual quality assessment metrics
Ponomarenko, N. N., Лукин, В. В., & Zelensky, A. A. (2004) · Iris (Roma Tre University) · Cited by 989
A no-reference objective image sharpness metric based on the notion of just noticeable blur (JNB)
Ferzli, R., & Karam, L. J. (2009) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2011760 · Cited by 758
A no-reference perceptual blur metric
Marziliano, P., Dufaux, F., Winkler, S., & Ebrahimi, T. (2003) · Proceedings. International Conference on Image Processing · doi:10.1109/icip.2002.1038902 · Cited by 557
${\bf S}_{3}$: A Spectral and Spatial Measure of Local Perceived Sharpness in Natural Images
Vu, C. T., Phan, T. D., & Chandler, D. M. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2169974 · Cited by 387
Self-citationA no-reference perceptual image sharpness metric based on a cumulative probability of blur detection
Narvekar, N. D., & Karam, L. J. (2009) · 2009 International Workshop on Quality of Multimedia Experience · doi:10.1109/qomex.2009.5246972 · Cited by 208
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No-reference image sharpness assessment based on local phase coherence measurement
Hassen, R., Wang, Z., & Salama, M. M. A. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5496297 · Cited by 114
Cited by
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment
Hosu, V., Lin, H., Sziranyi, T., & Saupe, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2967829 · Cited by 759
No-Reference Image Blur Assessment Based on Discrete Orthogonal Moments
Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., & Kot, A. C. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2392129 · Cited by 270
CVD2014—A Database for Evaluating No-Reference Video Quality Assessment Algorithms
Nuutinen, M., Virtanen, T., Vaahteranoksa, M., Vuori, T., Oittinen, P., & Hakkinen, J. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2562513 · Cited by 227
Deep neural network for blind visual quality assessment of 4K content
Lu, W., Sun, W., Min, X., Zhu, W., Zhou, Q., He, J., Wang, Q., Zhang, Z., Wang, T., & Zhai, G. (2023) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2022.3221689 · Cited by 60
Metadata
- Authors
- Vu, P. V., & Chandler, D. M.
- Venue
- IEEE Signal Processing Letters
- Published
- 2012-07
- Volume
- 19
- Issue
- 7
- Pages
- 423-426
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W1984066865
- Cited by
- 271
- References
- 15
- FWCI
- 13.11
- Type
- journal article
- Language
- en
- Open access
- closed
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