Welcome to Francis Academic Press

International Journal of Frontiers in Medicine, 2022, 4(1); doi: 10.25236/IJFM.2022.040101.

A Brief Review of Image Quality Assessment Techniques for Laparoscopic Image Restoration

Author(s)

Xiangwei Qin1,2, Zhengyi Zhong1,3, Hanze Wei1, Rongrong Liu1, Qun Wang1

Corresponding Author:
Qun Wang
Affiliation(s)

1Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China

2School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130022, China 

3School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 

Abstract

Laparoscopic images usually suffer image degradation due to smoking, insufficient illumination, specularity, and limited fields of view. Computer-assisted restoration algorithms have been developed to tackle this problem, whose performance needs to be assessed through a mechanism of image quality evaluation. In this study, we will briefly exam several image quality assessment technologies that have been utilized in the process of laparoscopic image restoration.

Keywords

Image quality assessment, Non-Reference IQA, Full-Reference IQA, Laparoscopic images

Cite This Paper

Xiangwei Qin, Zhengyi Zhong, Hanze Wei, Rongrong Liu, Qun Wang. A Brief Review of Image Quality Assessment Techniques for Laparoscopic Image Restoration. International Journal of Frontiers in Medicine (2022), Vol. 4, Issue 1: 1-6. https://doi.org/10.25236/IJFM.2022.040101.

References

[1] A. P. Twinanda, S. Shehata, D. Mutter, J. Marescaux, M. de Mathelin and N. Padoy, "EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos," in IEEE Transactions on Medical Imaging, vol. 36, no. 1, pp. 86-97, Jan. 2017.

[2] B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, "AOD-Net: All-in-One Dehazing Network," In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017.

[3] V. Venkatesh, N. Sharma, V. Srivastava, M. Singh, "Unsupervised smoke to desmoked laparoscopic surgery images using contrast driven Cyclic-DesmokeGAN," Computers in Biology and Medicine, Volume 123, 2020.

[4] J. Ho, S. Ermon, "Generative Adversarial Imitation Learning," Advances in Neural Information Processing Systems. 29: 4565-C4573, 2016.

[5] S. Salazar-Colores, H. M. Jiménez, C. J. Ortiz-Echeverri, and G. Flores, "Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel," in IEEE Access, vol. 8, pp. 208898-208909, 2020

[6] M. A. Azam, K. B. Khan, E. Rehman et al, "Smoke Removal and Image Enhancement of Laparoscopic Images by An Artificial Multi-Exposure Image Fusion Method," 26 October 2021.

[7] Lin, J, Jiang, M, Pang, Y, et al. "A desmoking algorithm for endoscopic images based on improved U-Net model, " Concurrency Computat Pract Exper. 2021.

[8] S. Bolkar, C. Wang, F. A. Cheikh and S. Yildirim, "Deep Smoke Removal from Minimally Invasive Surgery Videos," 2018 25th IEEE International Conference on Image Processing (ICIP), 2018, pp. 3403-3407

[9] C. Wang, A. Kedir Mohammed, F. Alaya Cheikh et al., "Multiscale deep desmoking for laparoscopic surgery," Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491Y, 2019.

[10] Vishal V., Sharma N., Singh M. "Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke". In: Zhou L. et al. (eds) OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. OR 2.0 2019, MLCN 2019. Lecture Notes in Computer Science, vol 11796. Springer, Cham.

[11] K. He, J. Sun and X. O. Tang, "Single image haze removal using dark channel prior," 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1956-1963.

[12] A Odena, "Semi-supervised learning with generative adversarial networks Workshop on Data-Efficient Machine Learning", ICML 2016.

[13] K. S and M. Durgadevi, "Generative Adversarial Network (GAN): a general review on different variants of GAN and applications," 2021 6th International Conference on Communication and Electronics Systems (ICCES), 2021, pp. 1-8.

[14] A. Baid, A. Kotwal, R. Bhalodia, S. N. Merchant, and S. P. Awate, "Joint Desmoking, Specularity Removal, and Denoising of Laparoscopy Images Via Graphical Models and Bayesian Inference" Indian Institute of Technology (IIT) Bombay. University of Utah., in IEEE 14th International Symposium on Biomedical Imaging, 2017, pp. 732–736.

[15] A. Kotwal, "Joint Desmoking and Denoising of Laparoscopy Images" Department of Electrical Engineering Indian Institute of Technology (IIT) Bombay Department of Computer Science and Engineering Indian Institute of Technology (IIT) Bombay,” in IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016, pp. 1050–1054.

[16] O. Sidorov, C. Wang, F. Alaya, "Generative Smoke Removal" Cheikh Proceedings of the Machine Learning for Health NeurIPS Workshop, PMLR 116:81-92, 2020.

[17] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image Quality Assessment: from Error Visibility to Structural Similarity," IEEE Transactions on Image Processing 13, 4, 600–612, 2004.

[18] Yan, Jia, J. Li, and X. Fu 2019 No-Reference Quality Assessment of Contrast-Distorted Images using Contrast Enhancement 2019 arXiv abs/1904.08879 (Preprint 1904.08879)

[19] A. Mittal, A. K. Moorthy and A. C. Bovik, "No-Reference Image Quality Assessment in the Spatial Domain," in IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695-4708, Dec. 2012.

[20] C. Wang, F. Alaya Cheikh, M. Kaaniche et al. "Variational based smoke removal in laparoscopic images," BioMed Eng OnLine 17, 139, 2018.

[21] R. Ferzli and L. J. Karam, "A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)," in IEEE Transactions on Image Processing, vol. 18, no. 4, pp. 717-728, April 2009.

[22] A. Mittal, R. Soundararajan and A. C. Bovik, "Making a Completely Blind Image Quality Analyzer," IEEE Signal processing Letters, pp. 209-212, vol. 22, no. 3, March 2013.