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Noise Generation Methods Preserving Image Color Intensity Distributions Cover

Noise Generation Methods Preserving Image Color Intensity Distributions

Open Access
|Sep 2022

References

  1. 1. Murai, Y, D. Whitney. Serial Dependence Revealed in History-Dependent Perceptual Templates. – Curr Biol., Vol. 31, 2021, No 14, pp. 3185-3191.e3. DOI: 10.1016/j.cub.2021.05.006.831910734087105
  2. 2. Bialek, W. Physical Limits to Sensation and Perception. – Annual Review of Biophysics and Biophysical Chemistry, Vol. 16, 1987, pp. 455-478.10.1146/annurev.bb.16.060187.002323
  3. 3. Faisal, A. A., L. P. Selen, D. M. Wolpert. Noise in the Nervous System. Nature Reviews. – Neuroscience, Vol. 9, 2008, No 4, pp. 292-303.10.1038/nrn2258
  4. 4. Sternad, D. It’s Not (Only) the Mean That Matters: Variability, Noise and Exploration in Skill Learning. – Curr. Opin. Behav. Sci., Vol. 20, 2018, pp. 183-195. DOI: 10.1016/j.cobeha.2018.01.004.605154530035207
  5. 5. Pelli, D. G., B. Farell. Why Use Noise? – Journal of the Optical Society of America. A, Optics, Image Science, and Vision, Vol. 16, 1999, No 3, pp. 647-653.10.1364/JOSAA.16.000647
  6. 6. Hu, X., Y. Qin, X. Ying et al. Temporal Characteristics of Visual Processing in Amblyopia. – Front Neurosci., Vol. 15, 2021, 673491. Published 3 Jun 2021. DOI: 10.3389/fnins.2021.673491.821108834149348
  7. 7. Mc Anany, J. J., J. C. Park, G. A. Fishman, R. A. Hyde. Contrast Sensitivity and Equivalent Intrinsic Noise in X-Linked Retinoschisis. – Transl. Vis. Sci. Technol., Vol. 11, 2022, No 3,7. DOI: 10.1167/tvst.11.3.7.891456735258559
  8. 8. Liu, R., M. Kwon. Increased Equivalent Input Noise in Glaucomatous Central Vision: Is It due to Undersampling of Retinal Ganglion Cells? – Invest Ophthalmol Vis. Sci., Vol. 61, 2020, No 8, 10. DOI: 10.1167/iovs.61.8.10.742573432645132
  9. 9. Braham Chaouche, A., D. Silvestre, A. Trognon, A. Arleo, R. Allard. Age-Related Decline in Motion Contrast Sensitivity due to Lower Absorption Rate of Cones and Calculation Efficiency. – Sci. Rep., Vol. 10, 2020, No 1, 16521. Published 5 October 2020. DOI: 10.1038/s41598-020-73322-7.753641533020552
  10. 10. Manning, C., M. S. Tibber, S. C. Dakin. Visual Integration of Direction and Orientation Information in Autistic Children. – ADLI, Vol. 2, 2017, pp. 1-16. DOI: 10.1177/2396941517694626.
  11. 11. Bocheva, N., I. Hristov, S. Stefanov, T. Totev, S. N. Staykova, M. S. Mihaylova. How the External Visual Noise Affects Motion Direction Discrimination in Autism Spectrum Disorder. – Behav Sci (Basel), Vol. 12, 2022, No 4, 113. Published 18 April 2022. DOI: 10.3390/bs12040113.903171035447685
  12. 12. Solomon, J. A., D. G. Pelli. The Visual Filter Mediating Letter Identification. – Nature, Vol. 36, 1994, No 9 (6479), pp. 395-397. https://doi.org/10.1038/369395a010.1038/369395a0
  13. 13. Ridder, W. H. 3rd. A Comparison of Contrast Sensitivity and Sweep Visual Evoked Potential (sVEP) Acuity Estimates in Normal Humans. – Doc. Ophthalmol., Vol. 139, 2019, No 3, pp. 207-219. DOI: 10.1007/s10633-019-09712-8.31414313
  14. 14. Vera-Diaz, F. A., P. J. Bex, A. Ferreira, A. Kosovicheva. Binocular Temporal Visual Processing in Myopia. – J. of Vis., Vol. 18, 2018, No 11, 17. DOI: 10.1167/18.11.17.620555930372727
  15. 15. Wang, H., G. E. Legge. Comparing the Minimum Spatial-Frequency Content for Recognizing Chinese and Alphabet Characters. – J. of Vis., Vol. 18, 2018, No 1, 1. DOI: 10.1167/18.1.1.574964829297056
  16. 16. Hussain, Z., P. J. Bennett. Perceptual Learning of Detection of Textures in Noise. – J. of Vis., Vol. 20, 2020, No 7, 22. DOI: https://doi.org/10.1167/jov.20.7.22.742495632692831
  17. 17. Lindborg, A., T. S. Andersen. Bayesian Binding and Fusion Models Explain Illusion and Enhancement Effects in Audiovisual Speech Perception. – PloS One., Vol. 16, 2021, No 2, e0246986. Published 19 February 2021. DOI: 10.1371/journal.pone.0246986.789537233606815
  18. 18. Ohnishi, M, K. Oda. Unresolvable Pixels Contribute to Character Legibility: Another Reason Why High-Resolution Images Appear Clearer. – Iperception, Vol. 11, 2020, No 6, 2041669520981102. Published 26 December 2020. DOI: 10.1177/2041669520981102.776832433489075
  19. 19. Eckstein, M. P., A. J. Ahumada. Classification Images: A Tool to Analyze Visual Strategies. – J. of Vis., Vol. 2, 2002, No 1, 1x. https://doi.org/10.1167/2.1.i10.1167/2.1.i
  20. 20. Levi, D. M., S. A. Klein. Noise Provides Some New Signals about the Spatial Vision of Amblyopes. – Journal of Neuroscience, Vol. 23, 2003, pp. 2522-2526.10.1523/JNEUROSCI.23-07-02522.2003
  21. 21. Allard, R., J. Faubert. Double Dissociation between First- and Second-Order Processing. – Vis. Res., Vol. 47, 2007, pp. 1129-1141. DOI: 10.1016/j.visres.2007.01.010.17363024
  22. 22. Allard, R., J. Faubert. First- and Second-Order Motion Mechanisms are Distinct at Low but Common at High Temporal Frequencies. – J. of Vis., Vol. 8, 2008, pp. 1-17. DOI: 10.1167/8.2.12.18318638
  23. 23. Drewes, J., W. Zhu, D. Melcher. The Optimal Spatial Noise for Continuous Flash Suppression Masking is Pink. – Sci. Rep., Vol. 10, 2020, 6943. https://doi.org/10.1038/s41598-020-63888-710.1038/s41598-020-63888-7718169632332984
  24. 24. Han, S., D. Alais. Strength of Continuous Flash Suppression is Optimal when Target and Masker Modulation Rates are Matched. – J. of Vis., Vol. 18, 2018, No 3, 3. DOI: https://doi.org/10.1167/18.3.3.29677318
  25. 25. Di Mattina, C., C. L. Baker. Modeling Second-Order Boundary Perception: A Machine Learning Approach. – PloS Comput Biol., Vol. 15, 2019, No 3, e1006829. Published 18 March 2019. DOI: 10.1371/journal.pcbi.1006829.643856930883556
  26. 26. Ahumada, A. J. Classification Image Weights and Internal Noise Level Estimation. – J. of Vis., Vol. 2, 2002, No 1, pp. 121-131. https://doi.org/10.1167/2.1.810.1167/2.1.812678600
  27. 27. Gold, J., A. Sekuler, P. Bennett. Characterizing Perceptual Learning with External Noise. – Cognitive Science, Vol. 28, 2004, pp. 167-207.10.1207/s15516709cog2802_3
  28. 28. Jeon, S. T., Z. L. Lu, B. A. Dosher. Characterizing Perceptual Performance at Multiple Discrimination Precisions in External Noise. – Journal of the Optical Society of America. A, Optica Publishing Group, Vol. 26, 2009, pp. B43-B58. https://doi.org/10.1364/JOSAA.26.000B4310.1364/JOSAA.26.000B43282944619884915
  29. 29. Taylor, C. P., P. J. Bennett, A. B. Sekuler. Evidence for Adjustable Bandwidth Orientation Channels. – Front. Psychol., Vol. 5, 2014, No 578. DOI: 10.3389/fpsyg.2014.00578.405401424971069
  30. 30. Gold, J. M. Information Processing Correlates of a Size-Contrast Illusion. – Front. Psychol., Vol. 5, 2014, No 142. DOI: 10.3389/fpsyg.2014.00142.392854024600430
  31. 31. Treviño, M., B. Dela Torre-Valdovinos, E. Manjarrez. Noise Improves Visual Motion Discrimination via a Stochastic Resonance-Like Phenomenon. – Frontiers in Human Neuroscience, Vol. 10, 2016, 572. https://doi.org/10.3389/fnhum.2016.0057210.3389/fnhum.2016.00572512010927932960
  32. 32. Benuci, A. Motor-Related Signals Support Localization Invariance for Stable Visual Perception. – PloS Computational Biology, Vol. 18, 2022, No 3, e1009928. https://doi.org/10.1371/journal.pcbi.100992810.1371/journal.pcbi.1009928894759035286305
  33. 33. Söderlund, G., J. Åsberg Johnels, B. Rothén, E. Torstensson-Hultberg, A. Magnusson, L. Fälth. Sensory White Noise Improves Reading Skills and Memory Recall in Children with Reading Disability. – Brain and Behavior, Vol. 11, 2021, No 7, e02114. https://doi.org/10.1002/brb3.211410.1002/brb3.2114832303234096202
  34. 34. Park, W. J., K. B. Schauder, R. Zhang, L. Bennetto, D. Tadin. High Internal Noise and Poor External Noise Filtering Characterize Perception in Autism Spectrum Disorder. – Scientific Reports, Vol. 7, 2017, No 1, 17584.10.1038/s41598-017-17676-5573055529242499
  35. 35. Gao, X., E. A. Stine-Morrow, S. R. Noh, R. T. Eskew. Visual Noise Disrupts Conceptual Integration in Reading. – Psychonomic Bulletin & Review, Vol. 18, 2011, No 1, pp. 83-88.10.3758/s13423-010-0014-421327368
  36. 36. Wang, Zh., A. Bovik, H. Sheikh, E. Simoncelli. Image Quality Assessment: From Error Visibility to Structural Similarity. – IEEE Transactions on Image Processing, Vol. 13, April 2004, No 4, pp. 600-612. DOI: 10.1109/TIP.2003.819861.
  37. 37. Simoncelli, E. P., B. A. Olshausen. Natural Image Statistics and Neural Representation. – Annual Review of Neuroscience, Vol. 24, 2001, pp. 1193-1216. https://doi.org/10.1146/annurev.neuro.24.1.119310.1146/annurev.neuro.24.1.119311520932
  38. 38. Krause, M. R., C. C. Pack. Contextual Modulation and Stimulus Selectivity in Extrastriate Cortex. – Vision Research, Vol. 104, 2014, pp. 36-46. https://doi.org/10.1016/j.visres.2014.10.00610.1016/j.visres.2014.10.00625449337
  39. 39. Shtereva, K., M. Stefanova, N. Bocheva, B. Hadjiyska, T. Totev, M. Mihaylova. Grapheme-Level Errors in Reading Words and Pseudo-Words by Children and Adolescent with Autism Spectrum Disorder. – In: Proc. of International Conference Emotional and Behavioral Disorders, Albena, 2020, pp. 221-232. ISBN 978-954-9458-28-2.
  40. 40. Boyat, A., B. Joshi. A Review Paper: Noise Models in Digital Image Processing. – ArXiv, abs/1505.03489, 2015.
  41. 41. Suryanarayana, S., B. Deekshatulu, K. Lal Kishore, R. Kumar. Estimation and Removal of Gaussian Noise in Digital Images. – International Journal of Electronics and Communication Engineering, Vol. 5, 2012, pp. 23-33.
  42. 42. Boncelet, Ch. Image Noise Models. – In Bovik, A. C. Handbook of Image and Video Processing, 2005.
  43. 43. Goodman, J. Some Fundamental Properties of Speckle. – Journal of the Optical Society of America, Vol. 66, 1976, pp. 1145-1150.10.1364/JOSA.66.001145
DOI: https://doi.org/10.2478/cait-2022-0031 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 111 - 126
Submitted on: May 4, 2022
Accepted on: Jun 6, 2022
Published on: Sep 22, 2022
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 Tsvetalin Totev, Nadejda Bocheva, Simeon Stefanov, Milena Slavcheva Mihaylova, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.