International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Hybrid Smoothing and Sharpening Filters Using the Spatial Domain: Literature Review

Abstract

This study explores the field of image processing, emphasizing the difficulty presented by exterior artefacts on film, such as scratches, bruises, and cracks. Due to the limitations of mathematical analysis, unlike homogeneous noise, the shape and location of these faults must be determined manually during picture processing. The tight balance between retaining minute details and removing undesirable effects like noise makes the search for a universal filtering algorithm ongoing. The proposed work presents the enhancing through removing noises by applying proposed smoothing technique in order to eliminate these noises, this step leads to the blurring case, so for removing this blurring case, the output of this step is considered as an input to the next step through applying a proposed sharpening algorithm in the field of spatial domain, the final step is grouping all the processed frames for retrieving the processed video segment, in order to measure the performance of the proposed work, we compare the proposed work with others through some determined metrics.

Country : Iraq

1 Mohammed M. Weli2 Omar M. Abdullah

  1. Computer Sciences Department, College of Computer Sciences and Mathematics, University of Mosul, Mosul - Iraq
  2. Computer Sciences Department, College of Computer Sciences and Mathematics, University of Mosul, Mosul - Iraq

IRJIET, Volume 8, Issue 2, February 2024 pp. 51-60

doi.org/10.47001/IRJIET/2024.802008

Full Paper
Download

References

  1. Rangayya, Virupakshappa, and N. Patil, “Facial Image Segmentation by Integration of Level Set and Neural Network Optimization with Hybrid Filter Pre-processing Model,” Eng. Sci., vol. 16, no. i, pp. 211–220, 2021, doi: 10.30919/es8d583.
  2. V. Acharya, V. Ravi, T. D. Pham, and C. Chakraborty, “Peripheral Blood Smear Analysis Using Automated Computer-Aided Diagnosis System to Identify Acute Myeloid Leukemia,” IEEE Trans. Eng. Manag., vol. 70, no. 8, pp. 2760–2773, 2021, doi: 10.1109/TEM.2021.3103549.
  3. G. Ramesh, J. Logeshwaran, J. Gowri, and A. Mathew, “The management and reduction of digital noise in video image processing by using transmission based noise elimination scheme,” ICTACT J. IMAGE VIDEO Process., no. 13, p. 1, 2022, doi: 10.21917/ijivp.2022.0398.
  4. G. Ramesh, J. Logeshwaran, J. Gowri, and A. Mathew, “The management and reduction of digital noise in video image processing by using transmission based noise elimination scheme,” ICTACT J. IMAGE VIDEO Process., no. 13, p. 1, 2022, doi: 10.21917/ijivp.2022.0398.
  5. Y. Dong and W. D. Pan, “A Survey on Compression Domain Image and Video Data Processing and Analysis Techniques,” Inf., vol. 14, no. 3, 2023, doi: 10.3390/info14030184.
  6. C. Orhei and R. Vasiu, “An Analysis of Extended and Dilated Filters in Sharpening Algorithms,” IEEE Access, vol. 11, pp. 81449–81465, 2023, doi: 10.1109/ACCESS.2023.3301453.
  7. C. S. K. Abdulah et al., “Review Study of Image De-Noising on Digital Image Processing and Applications,” J. Adv. Res. Appl. Sci. Eng. Technol., vol. 30, no. 1, pp. 331–343, 2023, doi: 10.37934/araset.30.1.331343.
  8. S. Agarwal and K. H. Jung, “Enhancing Low-Pass Filtering Detection on Small Digital Images Using Hybrid Deep Learning,” Electron., vol. 12, no. 12, 2023, doi: 10.3390/electronics12122637.
  9. A.Kumar Pathak and M. . Parsai, “A Study of Various Image Fusion Techniques,” Int. J. Eng. Trends Technol., vol. 15, no. 2, pp. 59–62, 2014, doi: 10.14445/22315381/ijett-v15p213.
  10. B. Alhassan, M. . Bagiwa, A. F. D. Kana, and M. Abdullahi, “a Survey of Image Denoising Filters Based on Boundary Discrimination Noise Detectio,” Fudma J. Sci., vol. 5, no. 4, pp. 12–21, 2022, doi: 10.33003/fjs-2021-0504-613.
  11. A.E. Ilesanmi and T. O. Ilesanmi, “Methods for image denoising using convolutional neural network: a review,” Complex Intell. Syst., vol. 7, no. 5, pp. 2179–2198, 2021, doi: 10.1007/s40747-021-00428-4.
  12. A.H. Lone and A. N. Siddiqui, “Noise models in digital image processing,” Glob. Sci-Tech, vol. 10, no. 2, p. 63, 2018, doi: 10.5958/2455-7110.2018.00010.1.
  13. N. Nazir, A. Sarwar, B. S. Saini, and R. Shams, “A Robust Deep Learning Approach for Accurate Segmentation of Cytoplasm and Nucleus in Noisy Pap Smear Images,” Computation, vol. 11, no. 10, 2023, doi: 10.3390/computation11100195.
  14. I.Singh and N. Neeru, “Performance Comparison of Various Image Denoising Filters under Spatial Domain,” Int. J. Comput. Appl., vol. 96, no. 19, pp. 21–30, 2014, doi: 10.5120/16903-6969.
  15. J. P. Arenas, “Impulse Noise: A Real Threat for Workers and a Challenge for Acousticians,” Int. J. Acoust. Vib., vol. 26, no. 4, pp. 272–273, 2021, doi: 10.20855/ijav.2021.26.4e102.
  16. K. Funo and A. Ishizaki, “Dynamics of a quantum system interacting with non-Gaussian baths: Poisson noise master equation,” arXiv:2312.00376v1, pp. 1–9, 2023, [Online]. Available: http://arxiv.org/abs/2312.00376
  17. K. Funo and A. Ishizaki, “Dynamics of a quantum system interacting with non-Gaussian baths: Poisson noise master equation,” arXiv:2312.00376v1, pp. 1–9, 2023.
  18. L. Fan, F. Zhang, H. Fan, and C. Zhang, “Brief review of image denoising techniques,” Vis. Comput. Ind. Biomed. Art, vol. 7, 2019.
  19. T. Rachman, “A study on image noise and various image denoising techniques,” Angew. Chemie Int. Ed. 6(11), 951–952., vol. 2, no. 11, pp. 10–27, 2018, doi: 10.17605/OSF.IO/87XGJ.
  20. N. H. Kaplan and I. Erer, Remote Sensing Image Enhancement via Robust Guided Filtering. 2019. doi: 10.1109/RAST.2019.8767443.
  21. N. Patel, A. Shah, M. Mistry, and K. Dangarwala, “A Study of Digital Image Filtering Techniques in Spatial Image Processing,” Int. Conf. Converg. Technol., pp. 1–7, 2014.
  22. Y. Abdoush, G. Pojani, and G. Corazza, “Adaptive Instantaneous Frequency Estimation of Multicomponent Signals Based on Linear Time-Frequency Transforms,” IEEE Trans. Signal Process., vol. 67, pp. 3100–3112, Apr. 2019, doi: 10.1109/TSP.2019.2912132.
  23. I.Stirb, “Chapter 9 - Highlight image filter significantly improves optical character recognition on text images☆,” L. Deligiannidis and H. R. B. T.-E. T. in I. P. Arabnia  Computer Vision and Pattern Recognition, Eds., Boston: Morgan Kaufmann, 2015, pp. 131–147. doi: https://doi.org/10.1016/B978-0-12-802045-6.00009-0.
  24. P. Li, H. Wang, M. Yu, and Y. Li, “Overview of Image Smoothing Algorithms,” J. Phys. Conf. Ser., vol. 1883, no. 1, 2021, doi: 10.1088/1742-6596/1883/1/012024.
  25. E. AYDOGAN DUMAN, “An Edge Preserving Image Denoising Framework Based on Statistical Edge Detection and Bilateral Filter,” Mehmet Akif Ersoy Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 12, no. Ek (Suppl.) 1, pp. 519–531, 2021, doi: 10.29048/makufebed.1029276.
  26. C.-C. Chang, J.-Y. Hsiao, and C.-P. Hsieh, “An Adaptive Median Filter for Image Denoising,” Intell. Inf. Technol. Appl. 2007 Work., vol. 2, pp. 346–350, Dec. 2008, doi: 10.1109/IITA.2008.259.
  27. S. Misra and Y. Wu, “Chapter 10 - Machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking,” S. Misra, H. Li, and J. B. T.-M. L. for S. C. He, Eds., Gulf Professional Publishing, 2020, pp. 289–314. doi: https://doi.org/10.1016/B978-0-12-817736-5.00010-7.
  28. S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, “Bilateral filtering: Theory and applications,” Found. Trends Comput. Graph. Vis., vol. 4, no. 1, pp. 1–73, 2009, doi: 10.1561/0600000020.
  29. O. Eisen, S. Rang, and A. Talvari, “DIGITAL IMAGE PROCESSING,” Eesti NSV Tead. Akad. Toim. Keemia. Geoloogia, vol. 23, no. 4, p. 307, 2023, doi: 10.3176/chem.geol.1974.4.04.
  30. A.Nasonov, A. Krylov, and D. Lyukov, “Image sharpening with blur map estimation using convolutional neural network,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 2/W12, pp. 161–166, 2019, doi: 10.5194/isprs-archives-XLII-2-W12-161-2019.
  31. K. Gupta and N. Goyal, “Fuzzy decision based median filter for removal of impulse noise,” Int. J. Eng. Adv. Technol., vol. 9, no. 1, pp. 2120–2124, 2019, doi: 10.35940/ijeat.A9671.109119.
  32. K. Vasanth, T. G. Manjunath, and S. N. Raj, “A Decision Based Unsymmetrical Trimmed Modified Winsorized Mean Filter for the Removal of High Density Salt and Pepper Noise in Images and Videos,” Procedia Comput. Sci., vol. 54, pp. 595–604, 2015, doi: 10.1016/j.procs.2015.06.069.
  33. P. Beniwal and T. Singh, “Image Enhancement by Hybrid Filter,” Int. J. Sci. Res. Manag., vol. 1, no. 5, pp. 292–295, 2013.
  34. C. Zhang, L. Ma, and L. Jing, “Mixed Frequency domain and spatial of enhancement algorithm for infrared image,” in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012, pp. 2706–2710. doi: 10.1109/FSKD.2012.6234042.
  35. S. S. Khan, Q. Ran, M. Khan, and Z. Ji, “Pan-Sharpening Framework Based on Laplacian Sharpening with Brovey,” ICSIDP 2019 - IEEE Int. Conf. Signal, Inf. Data Process. 2019, no. July 2022, 2019, doi: 10.1109/ICSIDP47821.2019.9173129.
  36. B. AKSOY and O. K. M. SALMAN, “A New Hybrid Filter Approach for Image Processing,” Sak. Univ. J. Comput. Inf. Sci., vol. 3, no. 3, pp. 334–342, 2020, doi: 10.35377/saucis.03.03.785749.
  37. J. Zhang, Y. Chen, and F. Huang, “Design of infrared image enhancement method based on high-low pass hybrid filtering,” Ninth Symp. Nov. Photoelectron. Detect. Technol. Appl. 126173F (4 April 2023), no. April, p. 179, 2023, doi: 10.1117/12.2665981.
  38. T. D. Pham, “Kriging-Weighted Laplacian Kernels for Grayscale Image Sharpening,” IEEE Access, vol. 10, pp. 57094–57106, 2022, doi: 10.1109/ACCESS.2022.3178607.
  39. G. Deng, F. Galetto, M. Alnasrawi, and W. Waheed, “A guided edge-aware smoothing-sharpening filter based on patch interpolation model and generalized gamma distribution,” IEEE Open J. Signal Process., vol. 2, no. April, pp. 119–135, 2021, doi: 10.1109/OJSP.2021.3063076.
  40. W. Liang, J. Long, K. C. Li, J. Xu, N. Ma, and X. Lei, “A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering,” ACM Trans. Multimed. Comput. Commun. Appl., vol. 17, no. 2, 2021, doi: 10.1145/3391297.
  41. N. Wangno and S. Pichai, “S N RU J ou rn al o f S cien ce a nd Tec h n o log y Hybrid Algorithm of Dark Chanel Prior and Guided filter for Single Image,” SNRU J our na l Sc i enc e Te ch no logy, vol. 2, no. l, pp. 182–189, 2020.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies