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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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General Research Article

A Distorted Light Field Image Correction Method Based on Improved Hough Transform

Author(s): Ruihua Zhang* and Shubo Bi

Volume 17, Issue 6, 2024

Published on: 04 December, 2023

Article ID: e041223224180 Pages: 11

DOI: 10.2174/0126662558270259231122040821

Price: $65

Abstract

Introduction: In using a camera to take photos, due to environmental limitations, uneven lighting can cause uneven distribution of the image light field, resulting in distortion of the image background and target, blurring of details, and distorted light field images.

Method: In view of this, research is conducted on the correction of distorted light field images based on the Hough transform. First, the improved Hough transform is utilized to locate the four coordinates, the matrix information of the normal image is applied to calculate the corresponding parameter amount, and then the low-frequency part of the image spectrum is removed. Finally, it uses the Gaussian function for difference, inputs the original data, and gets the correction result of the distorted light field image.

Result: The research results indicate that in the practical application of the distorted light field image correction algorithm based on the Hough transform, the improved Hough transform algorithm is superior to the traditional one.

Conclusion: In comparative experiments, the research algorithm outperforms the other three algorithms, with an average color restoration of 93.76% and an average signal-to-noise ratio of 54.22dB. The superiority of the research algorithm has been verified, indicating that the research method can perfectly correct distorted light field images and achieve good correction results.

[1]
Y. Gao, S. Yoon, R. Savjani, J. Pham, A. Kalbasi, A. Raldow, D.A. Low, P. Hu, and Y. Yang, "Comparison and evaluation of distortion correction techniques on an MR‐guided radiotherapy system", Med. Phys., vol. 48, no. 2, pp. 691-702, 2021.
[http://dx.doi.org/10.1002/mp.14634] [PMID: 33280128]
[2]
J. Coll-Font, S. Chen, R. Eder, Y. Fang, Q.J. Han, M. van den Boomen, D.E. Sosnovik, C. Mekkaoui, and C.T. Nguyen, "Manifold‐based respiratory phase estimation enables motion and distortion correction of free‐breathing cardiac diffusion tensor MRI", Magn. Reson. Med., vol. 87, no. 1, pp. 474-487, 2022.
[http://dx.doi.org/10.1002/mrm.28972] [PMID: 34390021]
[3]
C. Huang, F. Peng, and K. Liu, "Pipeline inspection gauge positioning system based on optical fiber distributed acoustic sensing", IEEE Sens. J., vol. 21, no. 22, pp. 25716-25722, 2021.
[http://dx.doi.org/10.1109/JSEN.2021.3116973]
[4]
X. Zhang, R. Mu, K. Chen, Y. Yang, and Y. Chen, "Intelligent hough transform with jaya to detect the diameter of red-hot circular workpiece", IEEE Sens. J., vol. 21, no. 1, pp. 560-567, 2021.
[http://dx.doi.org/10.1109/JSEN.2020.3015134]
[5]
J.A. Sánchez-Margallo, L. Tas, A. Moelker, J.J. van den Dobbelsteen, F.M. Sánchez-Margallo, T. Langø, T. van Walsum, and N.J. van de Berg, "Block‐matching‐based registration to evaluate ultrasound visibility of percutaneous needles in liver‐mimicking phantoms", Med. Phys., vol. 48, no. 12, pp. 7602-7612, 2021.
[http://dx.doi.org/10.1002/mp.15305] [PMID: 34665885]
[6]
G. Yang, J. Hu, Z. Hou, G. Zhang, and W. Wang, "A new hough transform operated in a bounded cartesian coordinate parameter space", IET Image Process., vol. 16, no. 8, pp. 2282-2295, 2022.
[http://dx.doi.org/10.1049/ipr2.12489]
[7]
Q. Huang, and J. Liu, "Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios", Int. J. Adv. Robot. Syst., vol. 18, no. 2, 2021.
[http://dx.doi.org/10.1177/17298814211008752]
[8]
R. Ahmad, S. Naz, and I. Razzak, "Efficient skew detection and correction in scanned document images through clustering of probabilistic hough transforms", Pattern Recognit. Lett., vol. 152, no. 12, pp. 93-99, 2021.
[http://dx.doi.org/10.1016/j.patrec.2021.09.014]
[9]
V.L. Coli, L. Gomart, D.F. Pisani, S. Cohen, L. Blanc-Féraud, J. Leblond, and D. Binder, "Microcomputed tomography for discriminating between different forming techniques in ancient pottery: New segmentation method and pore distribution recognition", Archaeometry, vol. 64, no. 1, pp. 84-99, 2022.
[http://dx.doi.org/10.1111/arcm.12693]
[10]
E. Catalano, A. Coscetta, E. Cerri, N. Cennamo, L. Zeni, and A. Minardo, "Automatic traffic monitoring by ϕ -OTDR data and Hough transform in a real-field environment", Appl. Opt., vol. 60, no. 13, pp. 3579-3584, 2021.
[http://dx.doi.org/10.1364/AO.422385] [PMID: 33983286]
[11]
J. Fastowicz, and K. Okarma, "Quality assessment of photographed 3D printed flat surfaces using hough transform and histogram equalization", J. Univers. Comput. Sci., vol. 25, no. 6, pp. 701-717, 2019.
[12]
O. Boudraa, W.K. Hidouci, and D. Michelucci, "Using skeleton and Hough transform variant to correct skew in historical documents", Math. Comput. Simul., vol. 167, no. 1, pp. 389-403, 2020.
[http://dx.doi.org/10.1016/j.matcom.2019.05.009]
[13]
Y. Tian, H. Zeng, J. Hou, J. Chen, J. Zhu, and K.K. Ma, "A light field image quality assessment model based on symmetry and depth features", IEEE Trans. Circ. Syst. Video Tech., vol. 31, no. 5, pp. 2046-2050, 2021.
[http://dx.doi.org/10.1109/TCSVT.2020.2971256]
[14]
J. Zhuang, Z. Wang, and B. Wang, "Video semantic segmentation with distortion-aware feature correction", IEEE Trans. Circ. Syst. Video Tech., vol. 31, no. 8, pp. 3128-3139, 2021.
[http://dx.doi.org/10.1109/TCSVT.2020.3037234]
[15]
O. Marcelot, F. Pace, P. Martin-Gonthier, O. Saint-Pe, M.B. de Boisanger, and P. Magnan, "Mitigation of parasitic light sensitivity in global shutter CMOS image sensors through use of correction frame", IEEE Trans. Electron Dev., vol. 68, no. 9, pp. 4491-4496, 2021.
[http://dx.doi.org/10.1109/TED.2021.3099451]
[16]
S. Liu, Y. Xiong, E. Dai, J. Zhang, and H. Guo, "Improving distortion correction for isotropic high‐resolution 3D diffusion MRI by optimizing Jacobian modulation", Magn. Reson. Med., vol. 86, no. 5, pp. 2780-2794, 2021.
[http://dx.doi.org/10.1002/mrm.28884] [PMID: 34121222]
[17]
Y. Guo, W. Zhang, D. Hou, Y. Yao, and S. Ge, "A fast coding method for distortion-free data hiding in high dynamic range image", J. Real-Time Image Process., vol. 16, no. 3, pp. 611-622, 2019.
[http://dx.doi.org/10.1007/s11554-019-00855-0]
[18]
Y. Akhtar, and D.P. Mukherjee, "Context‐based ensemble classification for the detection of architectural distortion in a digitised mammogram", IET Image Process., vol. 14, no. 4, pp. 603-614, 2020.
[http://dx.doi.org/10.1049/iet-ipr.2019.0639]
[19]
X. Wang, M. Cheng, J. Eaton, C-J. Hsieh, and S.F. Wu, "Fake node attacks on graph convolutional networks", J. Comput. Cognit. Eng., vol. 1, no. 4, pp. 165-173, 2022.
[http://dx.doi.org/10.47852/bonviewJCCE2202321]
[20]
S. Choudhuri, S. Adeniye, and A. Sen, "Distribution alignment using complement entropy objective and adaptive consensus-based label refinement for partial domain adaptation", Artif. Intell. Appl., vol. 1, no. 1, pp. 43-51, 2023.
[21]
G.V. Reddy, K. Deepika, L. Malliga, D. Hemanand, C. Senthilkumar, S. Gopalakrishnan, and Y. Farhaoui, "Human action recognition using difference of gaussian and difference of wavelet", Big. Data Mini. Analy., vol. 6, no. 3, pp. 336-346, 2023.
[http://dx.doi.org/10.26599/BDMA.2022.9020040]
[22]
Y. Wu, S. Misra, C. Sondergeld, M. Curtis, and J. Jernigen, "Machine learning for locating organic matter and pores in scanning electron microscopy images of organic-rich shales", Fuel, vol. 253, no. 10, pp. 662-676, 2019.
[http://dx.doi.org/10.1016/j.fuel.2019.05.017]

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