Image and Signal Processing: Bridging Theory with Practical Applications in the Digital Era
AUTHORS
L. Cai,Department of Chemical and Materials Engineering, University of Alberta, Edmonton T6G 2V4, Canada
K. Prashanthi,Department of Chemical and Materials Engineering, University of Alberta, Edmonton T6G 2V4, Canada
C. Montemagno,Department of Chemical and Materials Engineering, University of Alberta, Edmonton T6G 2V4, Canada
ABSTRACT
Image and signal processing has transformed from a theoretical framework into a dynamic field that powers numerous digital innovations. This paper examines the foundational theories, techniques, and applications of image and signal processing, exploring its relevance in an era of rapid technological advancement. Using a systematic review of the literature and case studies, this research addresses the core methods in image processing, such as enhancement, restoration, and compression, alongside signal processing techniques like filtering, sampling, and reconstruction. The study investigates practical applications across medical imaging, remote sensing, and audio and speech processing, showcasing how theoretical models apply to real-world scenarios. Results indicate that advancements in algorithmic efficiency, particularly through AI and machine learning integration, are key to overcoming the computational and data limitations traditionally associated with processing tasks. Furthermore, emerging challenges, such as data privacy and computational demand, are analyzed to emphasize the need for ethical and technical improvements in processing techniques. This paper underscores the significance of image and signal processing in shaping modern technology, predicting future trends toward quantum computing and real-time processing capabilities. By bridging theory with practical applications, the field continues to drive innovation, with far-reaching implications in healthcare, security, and beyond. This study contributes to a greater understanding of both the current impact and the future potential of image and signal processing technologies.
KEYWORDS
Image processing, Signal processing, Digital transformation, Algorithmic efficiency, Real-time processing
REFERENCES
[1] R. C. Gonzalez and R. E. Woods, “Digital image processing (4th ed.),” Pearson Education, (2018)
[2] Y. Zhang, L. Li, and X. Wang, “Recent advances in deep learning for image processing and computer vision applications,” IEEE Transactions on Neural Networks and Learning Systems, vol.31, no.4, pp.1171–1183, (2020)
[3] S. W. Smith, “Digital signal processing: A practical guide for engineers and scientists (2nd ed.),” Elsevier, (2019)
[4] Z. Xu, J. Wang, and M. Chen, “Quantum approaches in image and signal processing: A review,” Quantum Information Processing, vol.20, no.5, pp.167-178, (2021)
[5] A. Kumar, R. Patel, and T. Singh, “AI and quantum advancements in signal and image processing,” Journal of Advanced Computing, vol.32, no.3, pp.101-115, (2022)
[6] M. Petrou and C. Petrou, “Image processing: The fundamentals (3rd ed.),” Wiley, (2020)
[7] T. F. Chan and J. Shen, “Image processing and analysis: Variational, PDE, Wavelet, and Stochastic Methods (2nd ed.),” SIAM, (2022)
[8] S. Li, Y. Liu, and C. Yang, “Advances in adaptive filtering techniques for noise reduction in biomedical signal processing,” Journal of biomedical signal processing, vol.34, no.2, pp.145-159, (2023)
[9] J. G. Proakis and D. G. Manolakis, “Digital signal processing: Principles, algorithms, and applications (5th ed.),” Pearson, (2021)
[10] Z. Wang, A. C. Bovik, and H. R. Sheikh, “A comprehensive study on image quality assessment and enhancement,” IEEE Transactions on Image Processing, vol.31, no.9, pp.2235-2248, (2022)
[11] B. Chanda and D. Majumder, “Digital image processing and analysis: A practical approach,” Springer, (2021)
[12] J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.8, no.6, pp.679-698, (1986)
[13] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, “Digital image processing using MATLAB (4th ed.),” Pearson, (2020)
[14] K. Sayood, “Introduction to data compression (4th ed.),” Morgan Kaufmann, (2017)
[15] R C. Gonzalez, R. E. Woods, and S. L. Eddins, “Digital signal processing using MATLAB (4th ed.),” Pearson, (2019)
[16] R C. Harrison, S. Chen, and A. Smith, “Advanced modulation techniques for digital communications,” IEEE Transactions on Communications, vol.68, no.9, pp.5200-5215, (2020)
[17] S. Haykin, “Adaptive filter theory (5th ed.),” Pearson, (2019)
[18] Y. Huang and W. Wang, “Recent advances in adaptive signal processing techniques for noise reduction,” Journal of Signal Processing Systems, vol.94, no.3, pp.321-335, (2022)
[19] J. G. Proakis and D. G. Manolakis, “Digital signal processing: Principles, algorithms, and applications (5th ed.). Pearson, (2021)
[20] G. E. Dahl, D. Yu, and L. Deng, “Context-dependent pre-trained deep neural networks for large vocabulary speech recognition,” IEEE Transactions on Audio, Speech, and Language Processing, vol.29, pp.131-145, (2021)
[21] V. Dignum, “Responsible artificial intelligence: Designing AI for human values,” AI & Society, vol.36, no.3, pp.481-493, (2021)
[22] S. Khan, M. Awais, and M. Sadiq, “Deep learning in image processing: A review,” Journal of King Saud University - Computer and Information Sciences. Advance online publication, (2021)
[23] Y. Liu, D. Zhang, and H. Wang, “Privacy-preserving data mining: A survey,” IEEE Transactions on Knowledge and Data Engineering, vol.34, no.2, pp.294-314, (2022)
[24] W. Shi, H. Wang, and Y. Li, “Edge computing: A new view for the internet of things,” IEEE Internet of Things Journal, vol.7, vol.9, pp.7813-7826, (2020)
[25] Z. Zhang, M. Wu, and Q. Wang, “A survey on hybrid deep learning methods for image processing,” Information Fusion, vol.66, pp.54-66, (2021)
[26] Y. Zhou, M. Chen, and J. Xue, “An overview of image preprocessing techniques in medical imaging,” Journal of Medical Systems, vol.45, no.7, pp.1-11, (2021)