Digital Processing and Deep Learning Techniques: A review of the Literature

Authors

  • Kifaa Hadi Thanoon Northern Technical University
  • Abdulwahhb Fathee Shareef Northern Technical University
  • Omar A. Alsaif Northern Technical University

DOI:

https://doi.org/10.56286/ntujet.v1i3.223

Keywords:

Digital image processing, Deep Learning (DL), Segmentation, Classification, Feature extraction, Convolutional Neural Networks (CNN).

Abstract

This the first goal of digital image processing was to aid a human observer in intercepting certain events taking place through such images, yet we are more inclined to suppress the observer. We want to ask the computer to automatically analyze images in the same approach that a human observer might. Mathematical approaches are used to process images in image processing.Different approaches are done to the image in image processing to obtain a better image.The main goal of image enhancement is processing certain image so that the result is more appropriate for a specific application compared to original image. The first part of the presented study gives an overview regarding the approaches of digital image processing, while the second section introduces the concept of deep learning (DL) approaches and compares them.

References

Nurul Hakeem, Abd Rahim "Development of image processing software using visual basic 6.0" (2008) Faculty of Electrical & Electronic Engineering, Universiti Malaysia Pahang

Nurul H., Rajendra P., Ashutosh V." A Review on basics of Digital Image Processing" (2016) Jodhpur Institute of Engineering & Technology (JIET) Jodhpur, Rajasthan, International Journal of Engineering Research & Technology (IJERT).

Chitradevi,B. , Srimathi ,P. " An Overview on Image Processing Techniques " (2014) Dept. of Computer Science & Application , Thanthai Hans Roever College, Perambalur, India, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, Issue 11, November.

Priya K., Rasheeda Banu B., Umme Habiba T., Boosra A., (2019) " DIGITAL IMAGE PROCESSING TECHNIQUES-A REVIEW " (2014) PG Department of Computer Science, Tamil Nadu, JETIR, Volume 6, Issue 9.

Krizhevsky A., Sutskever I., Hinton G.,(2012) "ImageNet Classification with Deep Convolutional Neural Networks " . NIPS’12 Proc 25th Int Conf Neural Inf Process Syst 1:1097–1105.

Niall O’ M., Sean C., Anderson C., Suman H., Gustavo H., AND Joseph W., (2019) " Deep Learning vs. Traditional Computer Vision", IMaR Technology Gateway, Institute of Technology Tralee, Tralee, Ireland, pp. 128-144

Saleh, I.A., Alawsi, W.A., Alsaif, O.I., Alsaif, K.

, 2020,”A Prediction of Grain Yield Based on Hybrid Intelligent Algorithm", Journal, 1591(1), 012027

RAVI P., ASHOKKUMAR A.,(2017)" Analysis of Various Image Processing Techniques" , Department of Computer Science, Government Arts College for Women, Department of Computer Science, Alagappa Government Arts College, Karaikudi, International Journal of Advanced Networking & Applications (IJANA), Volume: 08, Issue: 05 Pages: 86-89 (2017) Special Issue.

Gaurav K., Pradeep K.,(2014)"A Detailed Review of Feature Extraction in Image Processing Systems" , Fourth International Conference on Advanced Computing & Communication Technologies.

Mary, A.H., Alsaif, O.I., Thanoon, K.H.,2022," Design and Implementation of Low-Cost Medical Auditory System of Distortion Otoacoustic Using Microcontroller", Journal of Engineering Science anTechnologythis link is disabled, 17(2), pp. 1068–1077.

Lazim M., Hashim N., Wong N. and Salleh A. (2014) "A Study on Image Enhancement" International Journal of Emerging Technology & Research, Volume 1, Issue 6, Sept - Oct.

Ravi P. ,Ashokkumar A. , (2015)“A study of various Data Compression Techniques” ,IJCS Journal, vol.6, Issue 2, April.

Kaur, A., (2014). “A review paper on image segmentation and its various techniques in image processing” International Journal of Science And Research, 3(12).

Ali, R.H., Khidhir, A.S.M.,2021,“Enhancement of Daytime Crescent Image Using Wiener Filter Based De-Blurring Technique”,7th International Conference on Contemporary Information Technology and Mathematics, ICCITM, pp. 203–206.

Yogamangalam R., (2013) "Segmentation Techniques Comparison in Image Processing", International Journal of Engineering and Technology (IJET), 5(1).

Saleh, I.A., Albayati, A.H., Thanoon, K.H., 2021,”Measure the Software Quality based on Grasshopper Optimization Algorithm”, International Journal of Computing and Digital Systems, 10(1), pp. 955–961

Kuruvilla D. ,Sukumaran A. ,Sankar S. ,and Joy,(2016)" A review on image processing and segmentation", international conference on data mining and advanced computing, (SAPIENCE), 198-203.

Kulkarni,P.M.,Naik,A.N.,Bhadvankar,A.P.,(2015)"Review Paper on Image Processing Techniques". International Journal for Scientific Research & Development,3(10).

Alhashmi, S.Q., Thanoon, K.H., Alsaif, O.I.,2020 “,A Proposed Face Recognition based on Hybrid Algorithm for Features Extraction”, Proceedings of the 6th International Engineering Conference ,Sustainable Technology and Development,pp.232-236,9122911.

Thanoon, K.H., Hasan, S.Q., Alsaif, O.I. “Biometric information based on distribution of arabic letters according to their outlet”. International Journal of Computing and Digital Systems, 2020, 90(5), pp. 981–991

LU,D., Weng,Q. (2007)“A survey of image classification methods and techniques for improving classification performance”, International Journal of Remote Sensing, 28(5), 823–870.

Oleiwi, Z., Thanoon, K., Alsaif, K.,2019”High frequency coefficient effect on image based on contourlet transformation”, IEEE International Conference on Computing and Information Science and Technology and their Applications 2019, 2019, 8830649

Reshma D., and Anup V., (2020)"A REVIEW ON DIGITAL IMAGE PROCESSING: APPLICATIONS, TECHNIQUES AND APPROACHES IN VARIOUS FIELDS", SVERIs College of Engineering, Pandharpur., International Journal of Advanced Research (IJAR).

Neetu R., (2017)" Image Processing Techniques: A Review", Computer Science Department, Chitkara University, Himachal Pradesh, Journal on Today’s Ideas – Tomorrow’s Technologies, Vol. 5, No. 1, June , pp. 40–49, www.chitkara.edu.in/publications.

Yahya, M.A., Alsaif, O.I., Saleh, I.A., ...Al-Yasir, Y.I.A., Abd-Alhameed, R.A. ,2019,”Noise cancellation for HIPERLAN/2 with open loop transmit diversity technique Inventions ,4(3), 46.

Mohammed, N.L., Aziz, M.S., AlSaif, O.I.,2020,” Design and implementation of robot control system for multistory buildings”,Telkomnika (Telecommunication Computing Electronics and Control)this link is disabled, 18(5), pp.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun, “Overfeat: Integrated recognition, localization and detection using convolutional networks,” arXiv:1312.6229, 2013. [45] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in CVPR, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR, 2016.

Zhong Z., Shou X., and Xindong W.,(2019) “Object Detection with Deep Learning: A Review”, IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS FOR PUBLICATION.

Amitha M. , Amudha P. and Sivakumari S.,(2020)" Deep Learning Techniques: An Overview", Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India, Advanced Machine Learning Technologies and Applications (pp.599-608).

Risto M., Jason L., Elliot M., Aditya R., and Nigel D.,(2019)" Evolving deep neural networks". In Artificial Intelligence in the Age of Neural Networks and Brain Computing, pages 293–312. Elsevier, doi: 10. 1016/B978-0-12-815480-9.00015-3.

Palash G., Sumit P., and Karan J.(2018)" Introduction to natural language processing and deep learning". In Deep Learning for Natural Language Processing, pages 1–74. Springer, doi: 10.1007/978-1-4842-3685-7 1.

Abhishek P., Yueru C., and Jay K.(2018)" Analysis on gradient propagation in batch normalized residual networks", arXiv preprint arXiv:1812.00342.

George S. and Alan L.(2012)" Linear regression analysis", John Wiley & Sons, volume 329.

Anil K Jain. Data clustering: 50 years beyond k-means. Pattern recognition letters, 31(8):651–666, 2010. doi: 10.1016/j.patrec.2009.09.011.

Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. Asynchronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928–1937, 2016.

Li Deng, Dong Yu, et al. Deep learning: methods and applications. Foundations and Trends R in Signal Processing, 7(3–4):197–387, 2014. doi: 10.1007/ 978-981-13-3459-7 3.

] Abhishek Panigrahi, Yueru Chen, and C-C Jay Kuo. Analysis on gradient propagation in batch normalized residual networks. arXiv preprint arXiv:1812.00342, 2018.

Jonathan Lorraine and David Duvenaud. Stochastic hyperparameter optimization through hypernetworks. arXiv preprint arXiv:1802.09419, 2018.

Nathan Hubens. Deep inside: Autoencoders - towards data science, Apr 2018.

Alessandro Achille and Stefano Soatto. Information dropout: Learning optimal representations through noisy computation. IEEE transactions on pattern analysis and machine intelligence, 40(12):2897–2905, 2018. doi: 10.1109/TPAMI.2017. 2784440.

Toshihiro Takahashi. Statistical max pooling with deep learning, July 3 2018. US Patent 10,013,644.

Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.

Risto Miikkulainen, Jason Liang, Elliot Meyerson, Aditya Rawal, Daniel Fink, Olivier Francon, Bala Raju, Hormoz Shahrzad, Arshak Navruzyan, Nigel Duffy, et al. Evolving deep neural networks. In Artificial Intelligence in the Age of Neural Networks and Brain Computing, pages 293–312. Elsevier, 2019. doi: 10. 1016/B978-0-12-815480-9.00015-3.

Ismael, A.K., Khidhir, A.M.,2020,“Evaluation of Transfer Learning with CNN to classify the Jaw Tumors”,IOP Conference Series: Materials Science and Engineering, 928(3), 032072

Additional Files

Published

2022-09-11

Issue

Section

Articles

How to Cite

[1]
“Digital Processing and Deep Learning Techniques: A review of the Literature”, NTU-JET, vol. 1, no. 3, Sep. 2022, doi: 10.56286/ntujet.v1i3.223.

Similar Articles

1-10 of 27

You may also start an advanced similarity search for this article.