Quantum Computing for Advanced Image Processing Applications

Authors

  • Mamatha B, Akkisetti Vn Hanuman, Tadi Chandra Sekhar, P Ravitej

Keywords:

Quantum Computing, Image Processing, Quantum Algorithms, Image, Compression, Pattern Recognition, Quantum Image Enhancement, Quantum Image Recovery, Scalability Challenges, Quantum Hardware, Future Research, Directions.

Abstract

Image processing is one of the many computer domains that has seen quantum computing emerge as a revolutionary technology. This is due to the fact that quantum computing holds the potential of exponential speedups and unique techniques. The purpose of this study is to give a thorough analysis of the applications of quantum computing in image processing. It investigates the ways in which quantum algorithms and hardware might be used to handle traditional issues, such as picture compression, enhancement, pattern detection, and image recovery. Within the scope of this study, the theoretical underpinnings of quantum computing are investigated, recent developments in the field are discussed, and a comparison is made between quantum techniques and classical procedures. In addition to this, it outlines the primary obstacles, such as scalability and hardware limits, that are now preventing the broad use of quantum approaches in image processing. In conclusion, the study provides an overview of prospective future research areas, focusing on the possibility of further integrating quantum computing with sophisticated image processing technologies such as deep learning. The purpose of this work is to provide scholars and practitioners who are interested in the interface of quantum computing and image processing with a basic reference that they may use.

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References

Buiten, H. J. and Clevers, J. G. P. W. “Land Observation by Remote Sensing Theory and Applications”, Gordon and Breach Science Publishers, Reading, pp. 642, 1993.

Cleve, R., Ekert, A., Machiavello, C. and Mosca, M., “Quantum algorithms revisited”, Proc.R.Soc.London A. Vol. 454, pp. 339354, 1998.

Woodell, G., Jobson, D., Rahman, Z. U. and Hienes May, G. “Advance Image processing of Arieal Imagery” in proc. SPIE Visual inform. Process. XIV Kissimmee, pp. 62460E, 2006.

Vorobel, R. “Contrast Enhancement of Remotely sensed images”, in 6 thInl. Conf. Math. Methods in Electromagnetic theory, LVIV, Ukraine, Sep., pp.472-475, 1996.

Karadakar, U., Nordt, M., Furuta, R. Lee, C. and Quick, C. “An Exploration of space – time constraints on contextual information in Image – Based testing interfaces”, Proc. ECDL 2006, Springer Berlin / Heidelberg, pp. 391-402, 2006.

Dimov, A., Jager, G. and Frangos, P. “Adaptive Edge Enhancement in SAR Images Training on the Data VS Training on simulated data”, Inter conf. image process, Thessaloniki, Creece, pp. 493-496, 2001.

Chen Chi Farn, Hung Yuchang and Li-Yu Chang, “A fuzzy based method for remote sensing image contrast enhancement”, The International Archives of the photogrammetry, Remote Sensing and spatial information science, Vol. XXXVII. Part B2, Beijing, 2008.

Chettri, S. R., Cromp, R. F. and Birhmingham, M. “Design of neutral networks for classification of remotely sensed imager” Telematics and informatics, Vol. 9. No. 3, pp. 145-156, 1992.

NaikSarif Kumar and Murthy, C. A. “Hue preserving color image enhancement without Gamut Problem”, IEEE Transactions on Image processing, Vol. 12(12), pp.1591-1598, 2003.

WangYazhen, “Statistical Analysis of Quantum Simulation”, Annals of Applied Statistics, Vol. 5, pp. 1- 20, 2011.

Pang Chao Yang, Zheng-WeiZhou and Guang-can Guo, “Quantum Discrete Cosine Transform for image compression”, Available at: http://arxiv.org /PS_cache/ quant ph/pdf/0601/0601043v2. pdf .2006.

RamitaManandhar, Inakwu, O. A., Odeh and TihoAncev, “Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement”, Remote Sensing. pp. 330-344, 2009.

SauKartik, AmitabhaChanda and Milan Pal, “Color Image Enhancement Based on Wavelet Transform and Human Visual System”, IEMCON 2011 organised in collaboration with IEEE, pp. 77-81, 2011.

Zhou Wang, Alan Conrad Bovik, Hamid Rahim Sheikh and Eero P. Simoncelli, “Image quality assessment: From error visibility to structural similarity”. IEEE Transaction on Image Processing, Vol. 13(4), pp. 600-612, 2004.

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Published

30.10.2024

How to Cite

Mamatha B. (2024). Quantum Computing for Advanced Image Processing Applications. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 5614 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7485

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

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