High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold
Source abstract
Abstract
Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in poor detail reservation performance under strong noise and the judgment performance of different noise characteristics has strong dependence and rather weak robustness. In order to reduce the effects of high-density salt and pepper noise on image quality when processing high-noise grayscale images, an improved two-dimensional maximum Shannon entropy median filter (TSETMF) is proposed for the adaptive selection of a threshold to enhance the filter performance while stably and effectively retaining the details of the images. The framework of the proposed improved TSETMF algorithm is designed in detail. The noise in images is filtered by means of automatically partitioning a window size, the threshold value of which is adaptively calculated using two-dimensional maximum Shannon entropy. The theoretical model is verified and analyzed through comparative experiments using three kinds of classical grayscale images. The experimental results demonstrate that the proposed improved TSETMF algorithm exhibits better processing performance than that of the traditional filter, with a higher suppression of high-density noise and denoising stability. This stronger ability while processing high-density noise is demonstrated by a higher peak signal-to-noise ratio (PSNR) of 24.97 dB with a 95% noise density located in the classical Lena grayscale image. The better denoising stability, with a noise density from 5% to 95%, is demonstrated by the minor decline in the PSNR of approximately 10.78% relative to a PSNR of 23.10 dB located in the classical Cameraman grayscale image. Furthermore, it can be advanced to promote higher noise filtering and stability for processing high-density salt and pepper noise in grayscale images.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image and Signal Denoising Methods · Advanced Image Fusion Techniques · Image Enhancement Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
Funding
Key Science and Technology Research Project of Henan Province · Doctoral Research Fund of Zhengzhou University of Light Industry
References (67)
Show source-provided references
1. Adaptive sequentially weighted median filter for image highly corrupted by impulse noise10.1109/ACCESS.2019.2950348 ↗
2. Live detection of face using machine learning with multi-feature method10.1007/s11277-018-5913-0 ↗
3. An image segmentation method based on two-dimensional entropy and chaotic lightning attachment procedure optimization algorithm10.1142/S0218001420540300 ↗
4. Mahdaoui, A.E., Ouahabi, A., and Moulay, M.S. (2022). Image denoising using a compressive sensing approach based on regularization constraints. Sensors, 22.10.3390/s22062199 ↗
5. Statistical based impulsive noise removal in digital radiography10.1109/TMI.2008.922698 ↗
6. Goceri, E. (2022). Evaluation of denoising techniques to remove speckle and Gaussian noise from dermoscopy images. Comput. Biol. Med., 152.10.1016/j.compbiomed.2022.106474 ↗
7. A universal noise removal algorithm with an impulse detector10.1109/TIP.2005.857261 ↗
8. Switching-based clustering algorithms for segmentation of low-level salt-and-pepper noise–corrupted images10.1007/s11760-013-0455-0 ↗
9. A two-stage filter for high density salt and pepper denoising10.1007/s11042-020-08887-6 ↗
10. Li, T., Wang, W., Xu, L., and Feng, X. (2014, January 15–16). Image denoising using low-rank dictionary and sparse representation. Proceedings of the 2014 Tenth International Conference on Computational Intelligence and Security, Kunming, China.10.1109/CIS.2014.56 ↗
11. A new adaptive center weighted median filter for suppressing impulsive noise in images10.1016/j.ins.2006.07.030 ↗
12. Vijayarajan, R., and Muttan, S. (2011, January 9–11). Iterative relaxed median filter for impulse noise removal and validation of fcm clustering using cluster error index in median filtered MR images. Proceedings of the 4th ObCom International Conference on Recent Trends in Computing, Communication and Information Technology, Vellore, India.10.1007/978-3-642-29219-4_88 ↗
13. Thilagavathi, M., and Deepa, P. (2013, January 21–22). An efficient dictionary learning algorithm for 3D Medical Image Denoising based on SADCT. Proceedings of the 2013 International Conference on Information Communication and Embedded Systems (ICICES), Chennai, India.10.1109/ICICES.2013.6508257 ↗
14. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images10.1364/PRJ.416437 ↗
15. Signal denoising with the use of the wavelet transform and the Kalman filter10.1134/S1064226914120158 ↗
16. Image denoising using trivariate shrinkage filter in the wavelet domain and joint bilateral filter in the spatial domain10.1109/TIP.2009.2026685 ↗
17. Speckle denoising in digital holography by nonlocal means filtering10.1364/AO.52.00A195 ↗
18. Modified pyramid dual tree direction filter-based image denoising via curvature scale and nonlocal mean multigrade remnant filter10.1002/dac.3486 ↗
19. Different applied median filter in salt and pepper noise10.1016/j.compeleceng.2018.01.019 ↗
20. Salt-and-pepper noise removal by adaptive median-based lifting filter using second-generation wavelets10.1007/s11760-011-0210-3 ↗
21. Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction10.1109/LSP.2009.2038769 ↗
22. Shukla, A.K., Bhateja, V., Verma, R.L., and Alam, M.S. (2014, January 6–8). An Improved Directional Weighted Median Filter for Restoration of Images Corrupted with High Density Impulse Noise. Proceedings of the 2014 International Conference on Reliability Optimization and Information Technology (ICROIT), Faridabad, India.10.1109/ICROIT.2014.6798376 ↗
23. Peng, B., Zhou, Y., Li, Q., Lin, M., Weng, J., and Zeng, Q. (2022, January 5–9). FPGA Implementation of Low-Latency Recursive Median Filter. Proceedings of the 2022 International Conference on Field-Programmable Technology (ICFPT), Hong Kong, China.10.1109/ICFPT56656.2022.9974273 ↗
24. Random-valued impulse noise removal using adaptive dual threshold median filter10.1016/j.jvcir.2014.10.004 ↗
25. A Hybrid Method for the Removal of RVIN Using Self Organizing Migration with Adaptive Dual Threshold Median Filter10.1007/s11220-023-00414-9 ↗
26. Goyal, P., Chaurasia, V., and Meena, O.P. (2017, January 27–29). Impulse Noise Removal with Zero’s Padding by Median Based Adaptive Filter. Proceedings of the 2017 International Conference on Recent Innovations in Signal Processing and Embedded Systems (RISE), Bhopal, India.10.1109/RISE.2017.8378129 ↗
27. How multiplicity determines entropy and the derivation of the maximum entropy principle for complex systems10.1073/pnas.1406071111 ↗
28. A practical computational framework for the multidimensional moment-constrained maximum entropy principle10.1016/j.jcp.2005.05.008 ↗
29. A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm10.1109/ACCESS.2022.3215082 ↗
30. Automatic image thresholding based on Shannon entropy difference and dynamic synergic entropy10.1109/ACCESS.2020.3024718 ↗
31. Chiranjeevi, K., Jena, U.R., and Harika, A. (2016, January 10–11). Image Compression Using Shannon Entropy-Based Image Thresholding. Proceedings of the 3rd International Conference on Computational Intelligence in Data Mining (ICCIDM), Bhubaneswar, India.
32. Maleki, Y.K., Safizadeh, M.S., Khajavi, M.N., Payganeh, G., and Shuruni, S. (2016, January 21–24). Over Hang Slant Cracked Rotor Vibration Signal Processing Based on Discrete Wavelet Transform. Proceedings of the 2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), Leipzig, Germany.10.1109/SSD.2016.7473655 ↗
33. Thresholding noise-free ordered mean filter based on Dempster-Shafer theory for image restoration10.1109/TCSI.2006.869897 ↗
34. A study of dispersion in chromatic confocal microscopy using digital image processing10.1016/j.optlastec.2020.106414 ↗
35. Contrast enhancement using combined 1-D and 2-D histogram-based techniques10.1109/LSP.2017.2687945 ↗
36. Manda, M.P., and Kim, H.S. (2020). A fast image thresholding algorithm for infrared images based on histogram approximation and circuit theory. Algorithms, 13.10.3390/a13090207 ↗
37. Zhang, H., Liu, H., Shang, Z., and Li, R. (2015, January 23–25). Robust Image Denoising with an Improved Wavelet Threshold Method. Proceedings of the 2015 6th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.10.1109/ICSESS.2015.7339060 ↗
38. George, G., Oommen, R.M., Shelly, S., Philipose, S.S., and Varghese, A.M. (2018, January 2–3). A Survey on Various Median Filtering Techniques for Removal of Impulse Noise from Digital Image. Proceedings of the 2018 Conference on Emerging Devices and Smart Systems (ICEDSS), Tiruchengode, India.10.1109/ICEDSS.2018.8544273 ↗
39. Translation-invariant wavelet denoising of full-tensor gravity–gradiometer data10.1007/s11770-017-0649-2 ↗
40. Distribution distance-based threshold auto-tuning method for switching median filter10.1587/elex.7.1310 ↗
41. Tan, X., Jiang, J., and Zha, D. (2012, January 20–21). Threshold Switch Filter Design and Its Implementation in Image Denoising. Proceedings of the 5th International Conference on Machine Vision (ICMV)—Computer Vision, Image Analysis and Processing, Wuhan, China.10.1117/12.2014216 ↗
42. Zhang, J., Liao, Y., and Yan, L. (2018, January 4–8). An Improved Image Thresholding Method Based on Two-Dimensional Histogram. Proceedings of the 2018 13th World Congress on Intelligent Control and Automation (WCICA), Changsha, China.10.1109/WCICA.2018.8630713 ↗
43. A thresholding method based on two-dimensional Renyi’s entropy10.1016/j.patcog.2003.10.008 ↗
44. Yu, C., Dian-Ren, C., Yang, L., and Lei, C. (2010, January 6–7). Otsu’s Thresholding Method Based on Gray Level-Gradient Two-Dimensional Histogram. Proceedings of the 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010), Wuhan, China.
45. Two-dimensional histogram equalization and contrast enhancement10.1016/j.patcog.2012.03.019 ↗
46. Jensen–Fisher information and Jensen–Shannon entropy measures based on complementary discrete distributions with an application to Conway’s game of life10.1016/j.physd.2023.133822 ↗
47. Identification of localized defects and fault size estimation of taper roller bearing (NBC_30205) with signal processing using the Shannon entropy method in MATLAB for automobile industries applications10.1016/j.heliyon.2022.e12053 ↗
48. Yang, W., Cai, L., and Wu, F. (2020). Image segmentation based on gray level and local relative entropy two-dimensional histogram. PLoS ONE, 15.10.1371/journal.pone.0229651 ↗
49. On Shannon entropy and its applications10.1016/j.kjs.2023.05.004 ↗
50. Using decision tree, particle swarm optimization, and support vector regression to design a median-type filter with a 2-level impulse detector for image enhancement10.1016/j.ins.2012.01.020 ↗
51. Coupling denoising algorithm based on discrete wavelet transform and modified median filter for medical image10.1007/s11771-019-3987-9 ↗
52. An improved median filter10.1109/42.57773 ↗
53. Adaptive frequency median filter for the salt and pepper denoising problem10.1049/iet-ipr.2019.0398 ↗
54. Ganesh, M.S.R., Kadali, K.S., Bhukya, R., Palleswari, Y.T.R., Siva, A., and Pragaspathy, S. (2021, January 15–16). Design of Decision Based Recursive Weighted Median Filter with Exponential Weights. Proceedings of the 1st International Conference on Applied Mathematics, Modeling and Simulation in Engineering (AMSE), Virtual.10.1088/1742-6596/2089/1/012016 ↗
55. Guo, S., Wang, G., Han, L., Song, X., and Yang, W. (2022). COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter. Biomed. Signal Process. Control, 75.10.1016/j.bspc.2022.103552 ↗
56. Xie, Y., Zhu, Z., Zhang, X., and Wang, Q. (2014, January 4–6). An Adaptive Median Filter Using Local Texture Information in Images. Proceedings of the 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS), Taiyuan, China.10.1109/ICIS.2014.6912129 ↗
57. Nalli, P.K., Kadali, K.S., Bhukya, R., Palleswari, Y.T.R., Siva, A., and Pragaspathy, S. (2021, January 15–16). Design of Exponentially Weighted Median Filter Cascaded with Adaptive Median Filter. Proceedings of the 1st International Conference on Applied Mathematics, Modeling and Simulation in Engineering (AMSE), Virtual.10.1088/1742-6596/2089/1/012020 ↗
58. Pixel density based trimmed median filter for removal of noise from surface image10.1007/s13204-021-01950-0 ↗
59. Kunsoth, R., and Biswas, M. (2016, January 23–25). Modified Decision Based Median Filter for Impulse Noise Removal. Proceedings of the 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.10.1109/WiSPNET.2016.7566350 ↗
60. A Novel Gray Image Denoising Method Using Convolutional Neural Network10.1109/ACCESS.2022.3169131 ↗
61. (2023, July 01). Kodak Lossless True Color Image Suite. Available online: http://r0k.us/graphics/kodak/.
62. (2023, July 01). TEST IMAGES. Available online: https://ccia.ugr.es/cvg/dbimagenes/.
63. (2023, July 01). National Microbiology Data Center. Available online: http://nmdc.cn/nCov/en/.
64. Qi, H., Tan, S., and Li, Z. (2022). Anisotropic Weighted Total Variation Feature Fusion Network for Remote Sensing Image Denoising. Remote Sens., 14.10.3390/rs14246300 ↗
65. Anti-EMI noise digital filter design for a 60-kW five-level SiC inverter without fiber isolation10.1109/TPEL.2017.2710483 ↗
66. Alanazi, T.M., Berriri, K., Albekairi, M., Ben Atitallah, A., Sahbani, A., and Kaaniche, K. (2023). New Real-Time High-Density Impulsive Noise Removal Method Applied to Medical Images. Diagnostics, 13.10.3390/diagnostics13101709 ↗
67. An adaptive minimum-maximum value-based weighted median filter for removing high density salt and pepper noise in medical images10.1504/IJAHUC.2020.109795 ↗