Comprehensive Analysis of Image Filtering Techniques : Bridging Traditional Methods and Deep Learning Innovations for Enhanced Visual Processing

Authors

  • Mahi Agrawal  B.Tech Scholar, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, U.P, India
  • Ravi Shankar  B.Tech Scholar, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, U.P, India
  • Pragya Maurya  B.Tech Scholar, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, U.P, India
  • Ravi Gautam  B.Tech Scholar, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, U.P, India
  • Dr. Razia Sultan  Associate Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, U.P, India

Keywords:

Image Filtering, Deep Learning, CNN, Visual Processing

Abstract

Image filtering is a cornerstone in image processing and computer vision, utilized to enhance, analyze, and transform visual data for diverse applications. This paper explores the principles, methodologies, and applications of image filtering, focusing on both traditional techniques like Gaussian and median filters and advanced, data-driven methods enabled by deep learning. Emphasis is placed on how these techniques address challenges such as noise reduction, edge detection, and feature enhancement. The study provides a comprehensive review of filtering techniques, discusses emerging trends like adaptive and context-aware filtering, and presents a methodology for designing, training, and deploying image filtering models. By integrating theoretical insights and practical implementations, the paper aims to guide both academic and industrial advancements in image filtering while identifying future research directions for improving computational efficiency and real-time performance.

References

  1. Gonzalez, R. C., & Woods, R. E. (2018). *Digital Image Processing* (4th ed.). Pearson
  2. Jain, A. K. (1989). *Fundamentals of Digital Image Processing*. Prentice Hall. 
  3. Pratt, W. K. (2007). *Digital Image Processing: PIKS Inside* (4th ed.). Wiley. 
  4. Szeliski, R. (2010). *Computer Vision: Algorithms and Applications*. Springer. 
  5. Castleman, K. R. (1996). *Digital Image Processing*. Prentice Hall. 
  6. Marr, D., & Hildreth, E. (1980). Theory of Edge Detection. *Proceedings of the Royal Society B: Biological Sciences*, 207(1167), 187–217. 
  7. Russ, J. C. (2016). *The Image Processing Handbook* (7th ed.). CRC Press. 
  8. Oppenheim, A. V., & Schafer, R. W. (2009). *Discrete-Time Signal Processing*. Pearson. 
  9. Forsyth, D. A., & Ponce, J. (2012). *Computer Vision: A Modern Approach* (2nd ed.). Pearson. 
  10. Tomasi, C., & Manduchi, R. (1998). Bilateral Filtering for Gray and Color Images. *Proceedings of the 1998 IEEE International Conference on Computer Vision*, 839–846. 
  11. Paris, S., Kornprobst, P., Tumblin, J., & Durand, F. (2009). Bilateral Filtering: Theory and Applications. *Foundations and Trends in Computer Graphics and Vision*, 4(1), 1–73. 
  12. Perona, P., & Malik, J. (1990). Scale-Space and Edge Detection Using Anisotropic Diffusion. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 12(7), 629–639. 
  13. Mallat, S. (2008). *A Wavelet Tour of Signal Processing* (3rd ed.). Academic Press. 
  14. Lim, J. S. (1990). *Two-Dimensional Signal and Image Processing*. Prentice Hall. 
  15. Bracewell, R. N. (2000). *The Fourier Transform and Its Applications* (3rd ed.). McGraw-Hill. 
  16. Daugman, J. G. (1985). Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-Dimensional Visual Cortical Filters. *Journal of the Optical Society of America A*, 2(7), 1160–1169. 

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-05-25

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Mahi Agrawal, Ravi Shankar, Pragya Maurya, Ravi Gautam, Dr. Razia Sultan, " Comprehensive Analysis of Image Filtering Techniques : Bridging Traditional Methods and Deep Learning Innovations for Enhanced Visual Processing" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.705-711, May-June-2023.