Blind image quality assessment through anisotropy
Open accessAbstract
We describe an innovative methodology for determining the quality of digital images. The method is based on measuring the variance of the expected entropy of a given image upon a set of predefined directions. Entropy can be calculated on a local basis by using a spatial/spatial-frequency distribution as an approximation for a probability density function. The generalized Rényi entropy and the normalized pseudo-Wigner distribution (PWD) have been selected for this purpose. As a consequence, a pixel-by-pixel entropy value can be calculated, and therefore entropy histograms can be generated as well. The variance of the expected entropy is measured as a function of the directionality, and it has been taken as an anisotropy indicator. For this purpose, directional selectivity can be attained by using an oriented 1-D PWD implementation. Our main purpose is to show how such an anisotropy measure can be used as a metric to assess both the fidelity and quality of images. Experimental results show that an index such as this presents some desirable features that resemble those from an ideal image quality function, constituting a suitable quality index for natural images. Namely, in-focus, noise-free natural images have shown a maximum of this metric in comparison with other degraded, blurred, or noisy versions. This result provides a way of identifying in-focus, noise-free images from other degraded versions, allowing an automatic and nonreference classification of images according to their relative quality. It is also shown that the new measure is well correlated with classical reference metrics such as the peak signal-to-noise ratio.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Training-based no reference QA techniques perform at least as well as mean squared error.
“Training based NR QA techniques have resulted in algorithms that perform at least as well as mean squared error, which has the benefit of a reference image [9], [10].”
From RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012) · cites this paper as [10]
The blind image quality assessment index based on Renyi entropy anisotropy has not been thoroughly evaluated on a large and comprehensive dataset.
“In [11], blind image quality assessment indices that measure the anisotropy in the distorted image through the Renyi entropy are introduced. However, performance of this index has not been thoroughly evaluated on a large and comprehensive dataset.”
From RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 49,574 · see citing works
The Mathematical Theory of Communication
Shannon, C. E., Weaver, W., & Wiener, N. (1950) · Physics Today · doi:10.1063/1.3067010 · Cited by 9,875 · see citing works
On the Quantum Correction For Thermodynamic Equilibrium
Wigner, E. (1932) · Physical Review · doi:10.1103/physrev.40.749 · Cited by 9,283 · see citing works
A universal image quality index
Wang, Z., & Bovik, A. C. (2002) · IEEE Signal Processing Letters · doi:10.1109/97.995823 · Cited by 5,757 · see citing works
Image information and visual quality
Sheikh, H. R., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859378 · Cited by 4,104 · see citing works
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881959 · Cited by 3,018 · see citing works
Objective image fusion performance measure
Xydeas, C. S., & Petrović, V. (2000) · Electronics Letters · doi:10.1049/el:20000267 · Cited by 2,011 · see citing works
Information measure for performance of image fusion
Guihong, Q., Zhang, D., & Pingfan, Y. (2002) · Electronics Letters · doi:10.1049/el:20020212 · Cited by 1,441 · see citing works
An information fidelity criterion for image quality assessment using natural scene statistics
Sheikh, H. R., Bovik, A. C., & de Veciana, G. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.859389 · Cited by 1,399 · see citing works
Live image quality assessment database release 2
Sheikh, H. R. (2005) · Medical Entomology and Zoology · Cited by 1,276 · see citing works
On the Quantum Correction for Thermodynamic Equilibrium
Wigner, E. P. (1997) · Springer eBooks · doi:10.1007/978-3-642-59033-7_9 · Cited by 1,031 · see citing works
Why is image quality assessment so difficult?
Wang, Z., Bovik, A. C., & Lu, L. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.5745362 · Cited by 787 · see citing works
Zhang, Z., & Blum, R. S. (1999) · Proceedings of the IEEE · doi:10.1109/5.775414 · Cited by 723 · see citing works
No-reference quality assessment using natural scene statistics: JPEG2000
Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.854492 · Cited by 575 · see citing works
Statistics of natural images and models
Huang, J., & Mumford, D. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1999.786990 · Cited by 503 · see citing works
Perceptual blur and ringing metrics: application to JPEG2000
Marziliano, P., Frederic Dufaux, A., WINKLER, S. S., & Ebrahimi, T. (2003) · Signal Processing Image Communication · doi:10.1016/j.image.2003.08.003 · Cited by 491 · see citing works
A measure of some time–frequency distributions concentration
Stanković, L. (2001) · Signal Processing · doi:10.1016/s0165-1684(00)00236-x · Cited by 462 · see citing works
The principal components of natural images
Hancock, P., Baddeley, R., & Smith, L. S. (1992) · Network Computation in Neural Systems · doi:10.1088/0954-898x_3_1_008 · Cited by 239 · see citing works
Image fusion metric based on mutual information and Tsallis entropy
Cvejic, N., Canagarajah, C. N., & Bull, D. (2006) · Electronics Letters · doi:10.1049/el:20060693 · Cited by 232 · see citing works
Switkes, E., Mayer, M. J., & Sloan, J. A. (1978) · Vision Research · doi:10.1016/0042-6989(78)90232-8 · Cited by 189 · see citing works
Hansen, B. C., & Essock, E. A. (2004) · Journal of Vision · doi:10.1167/4.12.5 · Cited by 176 · see citing works
Review of Postprocessing Techniques for Compression Artifact Removal
Shen, M.-Y., & Kuo, C.-C. J. (1998) · Journal of Visual Communication and Image Representation · doi:10.1006/jvci.1997.0378 · Cited by 175 · see citing works
Time-frequency complexity and information
Flandrin, P., Baraniuk, R. G., & Michel, O. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1994.390031 · Cited by 103 · see citing works
Joint spatial/spatial-frequency representation
Jacobson, L. D., & Wechsler, H. (1988) · Signal Processing · doi:10.1016/0165-1684(88)90043-6 · Cited by 98 · see citing works
A statistical analysis of natural images matches psychophysically derived orientation tuning curves
Baddeley, R., & Hancock, P. (1991) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1991.0147 · Cited by 95 · see citing works
Renyi information and signal-dependent optimal kernel design
Sang, T.-H., & Williams, W. J. (2002) · International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1995.480344 · Cited by 75 · see citing works
Separating the chaff from the wheat: possible origins of the oblique effect
Keil, M. S., & Cristóbal, G. (2000) · Journal of the Optical Society of America A · doi:10.1364/josaa.17.000697 · Cited by 64 · see citing works
Self-citationThe principal components of natural images
Hancock, P., Baddeley, R., & Smith, L. S. (1992) · Network Computation in Neural Systems · doi:10.1088/0954-898x/3/1/008 · Cited by 52 · see citing works
Entropy Approach in the Analysis of Anisotropy of Digital Images
Kirsanova, E. N., & Sadovsky, M. G. (2002) · Open Systems & Information Dynamics · doi:10.1023/a:1019704411382 · Cited by 15 · see citing works
Multiresolution-information analysis for images
Román-Roldán, R., Quesada-Molina, J. J., & Martı́nez-Aroza, J. (1991) · Signal Processing · doi:10.1016/0165-1684(91)90085-w · Cited by 12 · see citing works
New relative multifractal dimension measures
Dansereau, R. M., & Kinsner, W. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2001.941276 · Cited by 9 · see citing works
Qu, Y., Zhao, Y., & Pu, Z. (2006) · Optical Engineering · doi:10.1117/1.2388924 · Cited by 7 · see citing works
Combined Superresolution and Blind Deconvolution
Šroubek, F., Cristóbal, G., & Flusser, J. (2006) · AIP conference proceedings · doi:10.1063/1.2361202 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 6
- [7]7. H. R. Sheikh, A. C. Bovik, and L. K. Cormack, 'Noreference quality assessment using natural scene statistics: JPEG2000,' IEEE Trans. Image Process. 14 , 1918-1927 (2005).
- [10]10. W. J. Williams, M. L. Brown, and A. O. Hero, 'Uncertainity, information and time-frequency distributions,' Proc. SPIE 1566 , 144-156 (1991).
- [17]17. N. Wiener, Cybernetics (Wiley, 1948).
- [18]18. A. Rényi, 'Some fundamental questions of information theory,' in Selected Papers of Alfréd Rényi , Pál Turán, ed. (Akadémiai Kiadó, 1976), Vol. 3, pp. 526-552 [Originally in Magy. Tud. Akad. III Oszt. Kózl. 10 , 251-282 (1960)].
- [21]21. R. Eisberg and R. Resnick, Quantum Physics (Wiley, 1974).
- [25]25. K. H. Brenner, 'A discrete version of the Wigner distribution function,' in Proceedings of EURASIP, Signal Processing II: Theories and Applications (North Holland, 1983), pp. 307-309.
Cited by
Sorted by citations
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064 · see citing works
Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
Moorthy, A. K., & Bovik, A. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2147325 · Cited by 1,763 · see citing works
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
Saad, M. A., Bovik, A. C., & Charrier, C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2191563 · Cited by 1,750 · see citing works
Blind/Referenceless Image Spatial Quality Evaluator
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2011) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/acssc.2011.6190099 · Cited by 446 · see citing works
Seven Challenges in Image Quality Assessment: Past, Present, and Future Research
Chandler, D. M. (2013) · ISRN Signal Processing · doi:10.1155/2013/905685 · Cited by 427 · see citing works
Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content
Zhu, X., & Milanfar, P. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2052820 · Cited by 380 · see citing works
A DCT Statistics-Based Blind Image Quality Index
Saad, M. A., Bovik, A. C., & Charrier, C. (2010) · IEEE Signal Processing Letters · doi:10.1109/lsp.2010.2045550 · Cited by 372 · see citing works
RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment
Soundararajan, R., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2166082 · Cited by 337 · see citing works
Blind Image Quality Assessment Using a General Regression Neural Network
Li, C., Bovik, A. C., & Wu, X. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2120620 · Cited by 292 · see citing works
Single Image Deraining: From Model-Based to Data-Driven and Beyond
Yang, W., Tan, R. T., Wang, S., Fang, Y., & Liu, J. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2995190 · Cited by 290 · see citing works
Blind image quality assessment by relative gradient statistics and adaboosting neural network
Liu, L., Hua, Y., Zhao, Q., Huang, H., & Bovik, A. C. (2015) · Signal Processing Image Communication · doi:10.1016/j.image.2015.10.005 · Cited by 283 · see citing works
Reduced- and No-Reference Image Quality Assessment
Wang, Z., & Bovik, A. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.942471 · Cited by 280 · see citing works
No-Reference Quality Assessment of Tone-Mapped HDR Pictures
Kundu, D., Ghadiyaram, D., Bovik, A. C., & Evans, B. L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2685941 · Cited by 182 · see citing works
No-reference image and video quality assessment: a classification and review of recent approaches
Shahid, M. U., Rossholm, A., Lövström, B., & Zepernick, H. (2014) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2014-40 · Cited by 178 · see citing works
Automatic no-reference quality assessment for retinal fundus images using vessel segmentation
Köhler, T., Budai, A., Kraus, M. F., Odstrčilík, J., Michelson, G., & Joachim Hornegger, A. (2013) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2013.6627771 · Cited by 178 · see citing works
No-reference image quality assessment in curvelet domain
Liu, L., Dong, H., Huang, H., & Bovik, A. C. (2014) · Signal Processing Image Communication · doi:10.1016/j.image.2014.02.004 · Cited by 178 · see citing works
Atmospheric Turbulence Mitigation Using Complex Wavelet-Based Fusion
Anantrasirichai, N., Achim, A., Kingsbury, N. G., & Bull, D. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2249078 · Cited by 147 · see citing works
Gao, X., Gao, F., Tao, D., & Li, X. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2271356 · Cited by 137 · see citing works
No-reference color image quality assessment: from entropy to perceptual quality
Chen, X., Zhang, Q., Lin, M., Yang, G., & He, C. (2019) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-019-0479-7 · Cited by 127 · see citing works
Semantic versus instance segmentation in microscopic algae detection
Ruiz-Santaquiteria, J., Bueno, G., Déniz, Ó., Vállez, N., & Cristóbal, G. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.103271 · Cited by 126 · see citing works
Self-citationVisual quality assessment algorithms: what does the future hold?
Moorthy, A. K., & Bovik, A. C. (2010) · Multimedia Tools and Applications · doi:10.1007/s11042-010-0640-x · Cited by 114 · see citing works
Quality Assessment for Comparing Image Enhancement Algorithms
Chen, Z., Jiang, T., & Tian, Y. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.384 · Cited by 111 · see citing works
Non-distortion-specific no-reference image quality assessment: A survey
Manap, R. A., & Shao, L. (2015) · Information Sciences · doi:10.1016/j.ins.2014.12.055 · Cited by 109 · see citing works
Vanmali, A. V., & Gadre, V. M. (2017) · Sadhana · doi:10.1007/s12046-017-0673-1 · Cited by 105 · see citing works
Zhang, Y., Moorthy, A. K., Chandler, D. M., & Bovik, A. C. (2014) · Signal Processing: Image Communication · doi:10.1016/j.image.2014.05.004 · Cited by 102 · see citing works
No-reference image quality assessment algorithms: A survey
Kamble, V. M., & Bhurchandi, K. M. (2015) · Optik · doi:10.1016/j.ijleo.2015.02.093 · Cited by 98 · see citing works
Nonlocal Tensor Completion for Multitemporal Remotely Sensed Images’ Inpainting
Ji, T.-Y., Yokoya, N., Zhu, X. X., & Huang, T.-Z. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2790262 · Cited by 95 · see citing works
Real-time in vivo computed optical interferometric tomography
Ahmad, A., Shemonski, N. D., Adie, S. G., Kim, H. S., Hwu, W., Carney, P. S., & Boppart, S. A. (2013) · Nature Photonics · doi:10.1038/nphoton.2013.71 · Cited by 88 · see citing works
Hyperspectral and Multispectral Image Fusion Using Factor Smoothed Tensor Ring Decomposition
Chen, Y., Zeng, J., He, W. P., Zhao, X.-L., & Huang, T. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3114197 · Cited by 81 · see citing works
PlaceRaider: Virtual Theft in Physical Spaces with SmartphonesPreprint
Templeman, R., Rahman, Z., Crandall, D., & Kapadia, A. C. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1209.5982 · Cited by 76 · see citing works
Otón, J., Pereiro, E., Perez-Berna, A. J., Millach, L., Sorzano, C. Ó. S., Marabini, R., & Carazo, J. M. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.005092 · Cited by 69 · see citing works
Adaptive Near-Infrared and Visible Fusion for Fast Image Enhancement
Awad, M., Elliethy, A. S., & Aly, H. A. (2019) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2019.2956873 · Cited by 66 · see citing works
Color in Image and Video Processing: Most Recent Trends and Future Research Directions
Trémeau, A., Tominaga, S., & Plataniotis, K. N. (2008) · EURASIP Journal on Image and Video Processing · doi:10.1155/2008/581371 · Cited by 66 · see citing works
Enhanced maximum curvature descriptors for finger vein verification
Syarif, M. A., Ong, T. S., Teoh, A. B. J., & Connie, T. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3315-4 · Cited by 65 · see citing works
Anantrasirichai, N., Nicholson, L. B., Morgan, J. E., Erchova, I. A., Mortlock, K. E., North, R. V., Albon, J., & Achim, A. (2014) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2014.06.012 · Cited by 62 · see citing works
No-reference/Blind Image Quality Assessment: A Survey
Xu, S., Jiang, S., & Min, W. (2016) · IETE Technical Review · doi:10.1080/02564602.2016.1151385 · Cited by 62 · see citing works
Wave-aberration control with a liquid crystal on silicon (LCOS) spatial phase modulator
Fernández, E. J., Prieto, P. M., & Artal, P. (2009) · Optics Express · doi:10.1364/oe.17.011013 · Cited by 57 · see citing works
SmartPhoto: A Resource-Aware Crowdsourcing Approach for Image Sensing with Smartphones
Wu, Y., Wang, Y., Hu, W., & Cao, G. (2015) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2015.2444379 · Cited by 57 · see citing works
No-reference assessment of blur and noise impacts on image quality
Cohen, E., & Yitzhak Yitzhaky, B. (2009) · Signal Image and Video Processing · doi:10.1007/s11760-009-0117-4 · Cited by 57 · see citing works
Wang, Y., Hu, W., Wu, Y., & Cao, G. (2014) · ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) · doi:10.1145/2632951.2632979 · Cited by 53 · see citing works
Low Rank Tensor Completion with Poisson Observations
Zhang, X., & Ng, M. K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3059299 · Cited by 52 · see citing works
Deep Learning Thermal Image Translation for Night Vision Perception
Liu, S., Gao, M., John, V., Liu, Z., & Blasch, E. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3426239 · Cited by 50 · see citing works
Kauba, C., Prommegger, B., & Uhl, A. (2018) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2018.8698588 · Cited by 46 · see citing works
Survey of information theory in visual quality assessment
Soundararajan, R., & Bovik, A. C. (2013) · Signal Image and Video Processing · doi:10.1007/s11760-013-0442-5 · Cited by 45 · see citing works
Computed optical interferometric tomography for high-speed volumetric cellular imaging
Liu, Y.-Z., Shemonski, N. D., Adie, S. G., Ahmad, A., Bower, A. J., Carney, P. S., & Boppart, S. A. (2014) · Biomedical Optics Express · doi:10.1364/boe.5.002988 · Cited by 45 · see citing works
Challenges in cloud based ingest and encoding for high quality streaming media
Aaron, A., Li, Z., Manohara, M., Lin, J. Y., Wu, E. C.-H., & Kuo, C. ‐. J. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351097 · Cited by 44 · see citing works
Effective X-ray micro computed tomography imaging of carbon fibre composites
Zwanenburg, E. A., Norman, D. G., Qian, C. C., Kendall, K. N., Williams, M. A., & Warnett, J. M. (2023) · Composites Part B Engineering · doi:10.1016/j.compositesb.2023.110707 · Cited by 41 · see citing works
Comparison-Based Image Quality Assessment for Selecting Image Restoration Parameters
Liang, H.-Y., & Weller, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2601783 · Cited by 35 · see citing works
Optimized Multistable Stochastic Resonance for the Enhancement of Pituitary Microadenoma in MRI
Singh, M., Verma, A. K., & Sharma, N. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2715078 · Cited by 41 · see citing works
Liu, J., Jung, H., Dubra, A., & Tam, J. (2017) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.16-21003 · Cited by 39 · see citing works
MRI and PET Image Fusion Using Fuzzy Logic and Image Local Features
Javed, U., Riaz, M. M., Ghafoor, A., Ali, S. S., & Cheema, T. A. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/708075 · Cited by 38 · see citing works
Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach
Ma, Z., Hu, T., & Shen, L. (2016) · International Journal of Advanced Robotic Systems · doi:10.5772/62257 · Cited by 37 · see citing works
Reconstruction of distorted underwater images using robust registration
Zhang, Z., & Xu, Y. (2019) · Optics Express · doi:10.1364/oe.27.009996 · Cited by 34 · see citing works
No-training, no-reference image quality index using perceptual features
Li, C., Ju, Y., Bovik, A. C., Wu, X., & Sang, Q. (2013) · Optical Engineering · doi:10.1117/1.oe.52.5.057003 · Cited by 32 · see citing works
Salient target detection based on pseudo-Wigner-Ville distribution and Rényi entropy
Xu, Y., Zhao, Y. Z., Jin, C., Qu, Z., Liu, L., & Sun, X. (2010) · Optics Letters · doi:10.1364/ol.35.000475 · Cited by 32 · see citing works
No-Reference Image Quality Assessment Using Image Statistics and Robust Feature Descriptors
Oszust, M. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2754539 · Cited by 32 · see citing works
Hyperspectral Image Super-Resolution Inspired by Deep Laplacian Pyramid Network
He, Z., & Liu, L. (2018) · Remote Sensing · doi:10.3390/rs10121939 · Cited by 31 · see citing works
Correction of geometrically distorted underwater images using shift map analysis
Halder, K. K., Paul, M., Tahtalı, M., Anavatti, S. G., & Murshed, M. (2017) · Journal of the Optical Society of America A · doi:10.1364/josaa.34.000666 · Cited by 31 · see citing works
A Novel Objective Image Quality Metric for Image Fusion Based on Renyi Entropy
Zheng, Y., Qin, Z., Shao, L., & Hou, X. (2008) · Information Technology Journal · doi:10.3923/itj.2008.930.935 · Cited by 30 · see citing works
Oblique illumination in microscopy: A quantitative evaluation
Sánchez, C., Cristóbal, G., Bueno, G., Blanco, S., Borrego‐Ramos, M., Olenici, A., Pedraza, A., & Ruiz-Santaquiteria, J. (2017) · Micron · doi:10.1016/j.micron.2017.11.006 · Cited by 30 · see citing works
Self-citationAn Optimized Cascaded Stochastic Resonance for the Enhancement of Brain MRI
Singh, M., Verma, A. K., & Sharma, N. (2018) · IRBM · doi:10.1016/j.irbm.2018.08.002 · Cited by 29 · see citing works
Deep learning techniques for atmospheric turbulence removal: a review
Hill, P., Anantrasirichai, N., Achim, A., & Bull, D. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11086-6 · Cited by 28 · see citing works
A Study on Image Quality in Polarization-Resolved Second Harmonic Generation Microscopy
Stanciu, S. G., Ávila, F. J., Hristu, R., & Bueno, J. M. (2017) · Scientific Reports · doi:10.1038/s41598-017-15257-0 · Cited by 28 · see citing works
XTEND: Extending the depth of field in cryo soft X-ray tomography
Otón, J., Pereiro, E., Conesa, J. J., Chichón, F. J., Luque, D., Rodríguez, J. M., Perez-Berna, A. J., Sorzano, C. Ó. S., Klukowska, J., Herman, G. T., et al. (2017) · Scientific Reports · doi:10.1038/srep45808 · Cited by 27 · see citing works
Statistical Evaluation of No-Reference Image Quality Assessment Metrics for Remote Sensing Images
Li, S., Yang, Z., & Li, H. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6050133 · Cited by 27 · see citing works
Corrupted Reference Image Quality Assessment of Denoised Images
Zhang, C., Wu, C., & Hirakawa, K. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2878326 · Cited by 27 · see citing works
Autofocus method for automated microscopy using embedded GPUs
Castillo-Secilla, J. M., Saval-Calvo, M., Medina-Valdés, L., Cuenca-Asensi, S., Martínez-Álvarez, A., Sánchez, C., & Cristóbal, G. (2017) · Biomedical Optics Express · doi:10.1364/boe.8.001731 · Cited by 26 · see citing works
Self-citationCurrent Trends and Advances in Image Quality Assessment
Okarma, K. (2019) · Elektronika ir Elektrotechnika · doi:10.5755/j01.eie.25.3.23681 · Cited by 25 · see citing works
Mo, J., de Groot, M., & de Boer, J. F. (2015) · Optics Express · doi:10.1364/oe.23.004935 · Cited by 25 · see citing works
Blind Image Quality Assessment by Visual Neuron Matrix
Chang, H.-W., Bi, X.-D., & Chen, K. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3106579 · Cited by 24 · see citing works
A dual-model approach to blind quality assessment of noisy images
Zhai, G., Kaup, A., Wang, J., & Yang, X. (2015) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2015.8 · Cited by 23 · see citing works
Non-reference image quality assessment for biometric presentation attack detection
Bhogal, A. P. S., Söllinger, D., Trung, P., & Uhl, A. (2017) · International Workshop on Biometrics and Forensics (IWBF) · doi:10.1109/iwbf.2017.7935080 · Cited by 23 · see citing works
Some new findings on the cumulative residual Tsallis entropy
Toomaj, A., & Atabay, H. A. (2021) · Journal of Computational and Applied Mathematics · doi:10.1016/j.cam.2021.113669 · Cited by 22 · see citing works
Visual Perception and Quality Assessment
Moorthy, A. K., Wang, Z., & Bovik, A. C. (2011) · Wiley eBooks · doi:10.1002/9783527635245.ch19 · Cited by 22 · see citing works
Analysis of controversies in the formulation and evaluation of restoration algorithms for MR Images
Simi, V. R., Edla, D. R., Joseph, J., & Kuppili, V. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.003 · Cited by 21 · see citing works
A Novel Variational Model for Detail-Preserving Low-Illumination Image Enhancement
Xu, Y., & Sun, B. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108468 · Cited by 21 · see citing works
Zhao, Y. S., Xiao, S., Yang, J., Lu, W. B., & Gao, X. (2022) · Signal Processing · doi:10.1016/j.sigpro.2022.108782 · Cited by 21 · see citing works
Blind noisy image quality assessment using block homogeneity
Huang, X., Chen, L., Tian, J., Zhang, X., & Fu, X. (2013) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2013.08.002 · Cited by 21 · see citing works
Robustness of speckle-imaging techniques applied to horizontal imaging scenarios
Bos, J. P., & Roggemann, M. C. (2012) · Optical Engineering · doi:10.1117/1.oe.51.8.083201 · Cited by 21 · see citing works
No-reference image quality assessment through the von Mises distribution
Gabarda, S., & Cristóbal, G. (2012) · Journal of the Optical Society of America A · doi:10.1364/josaa.29.002058 · Cited by 20 · see citing works
Self-citationNo-Reference quality assessment of noisy images with local features and visual saliency models
Oszust, M. (2019) · Information Sciences · doi:10.1016/j.ins.2019.01.034 · Cited by 20 · see citing works
Jensen–Shannon divergence for visual quality assessment
Bruni, V., Rossi, E., & Vitulano, D. (2013) · Signal Image and Video Processing · doi:10.1007/s11760-013-0444-3 · Cited by 20 · see citing works
Forensic and anti-forensic techniques for video shot editing in H.264/AVC
Su, P.-C., Suei, P.-L., Chang, M.-K. C., & Lain, J. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.02.006 · Cited by 19 · see citing works
Empirical wavelet transform‐based fog removal via dark channel prior
Sarkar, M., Sarkar, P. R., Mondal, U., & Nandi, D. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0496 · Cited by 19 · see citing works
Analysing neutron radiation damage in YBa2Cu3O7–x high‐temperature superconductor tapes
Linden, Y., Iliffe, W., He, G., Danaie, M., Fischer, D. X., Eisterer, M., Speller, S. C., & Grovenor, C. R. M. (2021) · Journal of Microscopy · doi:10.1111/jmi.13078 · Cited by 19 · see citing works
A novel approach for image dehazing combining visible-NIR images
Vanmali, A. V., Kelkar, S. G., & Gadre, V. M. (2015) · National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG) · doi:10.1109/ncvpripg.2015.7489945 · Cited by 18 · see citing works
No‐reference quality measure in brain MRI images using binary operations, texture and set analysis
Osadebey, M. E., Pedersen, M., Arnold, D. L., & Wendel-Mitoraj, K. E. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2016.0560 · Cited by 18 · see citing works
Söllinger, D., Trung, P., & Uhl, A. (2018) · IET Biometrics · doi:10.1049/iet-bmt.2017.0146 · Cited by 18 · see citing works
Non-Uniform Deblurring in HDR Image Reconstruction
Vijay, C. S., Chandramouli, P., Rajagopalan, A. N., & Chellappa, R. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2257809 · Cited by 18 · see citing works
Finger-Vein Image Enhancement Based on Pulse Coupled Neural Network
Lei, L., Xi, F., & Chen, S. (2019) · IEEE Access · doi:10.1109/access.2019.2914229 · Cited by 18 · see citing works
Anisotropic blind image quality assessment: Survey and analysis with current methods
Gabarda, S., Cristóbal, G., & Goel, N. (2018) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2018.02.008 · Cited by 17 · see citing works
Self-citationA no-reference metric to assess quality of denoising for Magnetic Resonance images
Simi, V. R., Edla, D. R., & Joseph, J. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102962 · Cited by 17 · see citing works
Sparsity-based no-reference image quality assessment for automatic denoising
Rakhshanfar, M., & Amer, M. A. (2017) · Signal Image and Video Processing · doi:10.1007/s11760-017-1215-3 · Cited by 17 · see citing works
No-reference light field image quality assessment based on depth, structural and angular information
Xiang, J., Jiang, G., Yu, M., Bai, Y., & Zhu, Z. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108063 · Cited by 16 · see citing works
Uddin, M. S., Tahtalı, M., Lambert, A. J., Pickering, M. R., Marchese, M., & Stuart, I. (2016) · Applied Optics · doi:10.1364/ao.55.004024 · Cited by 16 · see citing works
Video authentication in digital forensic
Gupta, A., Gupta, S., & Mehra, A. (2015) · International Conference on Futuristic Trends on Computational Analysis and Knowledge Management · doi:10.1109/ablaze.2015.7154945 · Cited by 16 · see citing works
No-reference image quality assessment based on noise, blurring and blocking effect
Zhou, L., & Zhang, Z.-B. (2014) · Optik · doi:10.1016/j.ijleo.2014.07.010 · Cited by 16 · see citing works
An entropy based approach for SSIM speed up
Bruni, V., & Vitulano, D. (2017) · Signal Processing · doi:10.1016/j.sigpro.2017.01.007 · Cited by 15 · see citing works
Water–air imaging: distorted image reconstruction based on a twice registration algorithm
Cai, C., Meng, H., Qiao, R., & Wang, F. (2021) · Machine Vision and Applications · doi:10.1007/s00138-021-01188-4 · Cited by 15 · see citing works
Noisy images-JPEG compressed: subjective and objective image quality evaluation
Corchs, S., Gasparini, F., & Schettini, R. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2039273 · Cited by 15 · see citing works
- Authors
- Gabarda, S., & Cristóbal, G.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- Venue
- Journal of the Optical Society of America A
- Publisher
- Optica Publishing Group
- Published
- 2007-09-26
- Volume
- 24
- Issue
- 12
- Pages
- B42
- ISSN
- 1084-7529 (ISSN-L)
- 1520-8532
- OpenAlex
- W2119999314
- Cited by
- 301
- References
- 33
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 8 · 2023: 8 · 2022: 9 · 2021: 17 · 2020: 14 · 2019: 18 · 2018: 28 · 2017: 39 · 2016: 28 · 2015: 26 · 2014: 22 · 2013: 25 · 2012: 20
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Journal of the Optical Society of America A (publishedVersion)
- PubMed (publishedVersion)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- no-reference image quality assessment, image quality index, anisotropy, Rényi entropy, pseudo-Wigner distribution, direction selectivity, image degradation, image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
