Adaptive window-based filter for high-density impulse noise suppression

被引：0
作者：
Rani S. [1 ]
Chabbra Y. [2 ]
Malik K. [3 ]
机构：
[1] Department of Computer Science and Application, CT University, Punjab, Ludhiana
[2] Department of Electronics and Communication, CT University, Punjab, Ludhiana
[3] Department of Computer Science and Engineering, CT University, Punjab, Ludhiana
来源：
Measurement: Sensors | 2022年 / 24卷
关键词：
Adaptive filter; Denoise; Impulse noise; Restoration;
D O I：
10.1016/j.measen.2022.100455
中图分类号：
学科分类号：
摘要：
One of the primary factors that lower image quality is noise. In the world of image processing, image denoising is a crucial image-enhancing operation. This paper introduces and presents an improved denoising technique based on an adaptive ROAD-TGM-HT (rank order absolute difference-trimmed global mean Hough transform) filter that effectively eliminates high-density salt and pepper noise. The proposed approaches are evaluated using the MATLAB program. In qualitative and quantitative statistical analyses, performance metrics such as the Peak Signal to Noise Ratio, Image Enhancement Factor, and Structural Similarity Index have been utilized. Performing image denoising using these performance criteria. Grayscale and color pictures are utilized to test the Proposed Algorithm (PA), which offers an exceptional PSNR and IEF, especially at rising noise densities. This method considerably enhances image quality in comparison to prior techniques by efficiently reducing high-density noise produced during image acquisition or resulting from data transfer. To overcome this issue, we strengthened the ROAD-TGM by making it adaptable, and for the remaining pixels, we added the Hough Transform method, allowing it to perform better in scenarios with a high noise density. © 2022 The Author(s)
引用
收藏
相关论文
共 35 条
  • [1] Chen C.L.P., Liu L., Chen L., Tang Y.Y., Zhou Y., Weighted couple sparse representation with classified regularization for impulse noise removal, IEEE Trans. Image Process., 24, pp. 4014-4026, (2015)
  • [2] Chen Q.-Q., Hung M.-H., Zou F., Effective and adaptive algorithm for pepper-and-salt noise removal, IET Image Process., 11, pp. 709-716, (2017)
  • [3] Gonzalez-Hidalgo M., Massanet S., Mir A., Ruiz-Aguilera D., Improving salt and pepper noise removal using a fuzzy mathematical morphology-based filter, Appl. Soft Comput., 63, pp. 167-180, (2018)
  • [4] Li Y., Chung F.L., Wang S., A robust neuro-fuzzy network approach to impulse noise filtering for color images, Appl. Soft Comput., 8, pp. 872-884, (2008)
  • [5] Nie L., Wang M., Zha Z.-J., Chua T.-S., Oracle in image search, ACM Trans. Inf. Syst., (2012)
  • [6] Wang G., Li D., Pan W., Zang Z., Modified switching median filter for impulse noise removal, Signal Process., 90, pp. 3213-3218, (2010)
  • [7] Deng X., Ma Y., Dong M., A new adaptive filtering method for removing salt and pepper noise based on multilayered PCNN, Pattern Recogn. Lett., 79, pp. 8-17, (2016)
  • [8] Filipovic M., Jukic A., Restoration of images corrupted by mixed Gaussian-impulse noise by iterative soft-hard thresholding, Eur. Signal Process. Conf., 44, pp. 1637-1641, (2014)
  • [9] Nair M.S., Raju G., Additive noise removal using a novel fuzzy-based filter, Comput. Electr. Eng., 37, pp. 644-655, (2011)
  • [10] Wang S., Liu Q., Xia Y., Dong P., Luo J., Huang Q., Feng D.D., Dictionary learning based impulse noise removal via L1-L1 minimization, Signal Process., 93, pp. 2696-2708, (2013)
1234