A Novel Approach on Impulse Noise Suppression Median-Mean Filter

Manpreet Singh, Harpreet Kaur

Abstract


Noise is the undesirable change that corrupts the information being transferred on the channel. All the communication channels are prone to noise, which makes it difficult to recover the original data or image at the receiving end. Noise cannot be completely suppressed on the channel part, this makes it even more challenging for the researchers to design the receiver that can regenerate the original information from the noise affected one. In this article an impulse noise suppression median-mean filter (INSMF) is designed for image data which works on three different window sizes to reduce the error between original and received values. To achieve a high degree of accuracy, not only the benefits of median filtering but also Normalized Euclidean Distance and mean value replacement are explored. Theoretical analysis and experimental results are drawn by taking different noise levels present in channel that determines the effectiveness of the proposed filtering method.


Keywords


Error flag matrix, Impulse noise, Image recovery, Median filtering, Noise suppression, Salt and pepper noise, Window method.

Full Text:

PDF

References


. Abreu E, Lighstone M, Mitra SK, et al. A new efficient approach for removal of impulse noise from highly corrupted images. IEEE Trans. Image Processing 1996; 5(6); 1012-25.

. You X, Crebbin G. A Robust Adaptive Estimator for Filtering Noise in Image. IEEE Transaction on Image Processing 1995; 4(5): 693-9.

. Bhukhanwala SA, Ramabhadran TV. Automated global enhancement of digitized photographs. IEEE Trans. Consumer Electronics 1994; 40(1): 1-10.

. Boo KJ, Bose NK. A Motion-Compensated Spatio-Temporal Filter for Image Sequences with Signal-Dependent Noise. IEEE Trans. On circuits and syatems for Video Technology 1998; 8(3): 287-98.

. Jiang XD. Image detail-preserving filter for impulsive noise attenuation. IEE Proceedings in Vision, Image and Signal Processing 2003; 150(3): 179-85.

. Van De Ville D, Van der Weken D, Kerre EE, et al. Noise reduction by fuzzy image filtering. IEEE Transactions on Fuzzy Systems 2003; 11(4): 429-36.

. Xu X, Miller EL, Chen D, et al. Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. IEEE Transactions on Image Processing 2004; 13(2): 238-47.

. Seong-Won L, Jang J, Shin J, et al. Noise-Adaptive Spatio-Temporal Filter for Real-Time Noise Removal in Low Light Level Images. IEEE Transactions on Consumer Electronics 2005; 51(2): 648-53.

. Pei-Eng N, Kai-Kuang M. A Switching Median Filter With Boundary Discriminative Noise Detection for Extremely Corrupted Images. IEEE Transactions on Image Processing 2006; 15(6): 1506-16.

. Schilham AMR, van Ginneken B, Gietema H, et al. Local Noise Weighted Filtering for Emphysema Scoring of Low-Dose CT Images. IEEE Transactions on Medical Imaging 2006; 25(4): 451-63.

. Tulin Yıldırım M, Basturk A, Emin Yuksel M. Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic. IEEE Transactions on Fuzzy Systems 2008; 16(4): 920-8.

. Ibrahim H, Sia Pik Kong N, Foo Ng T. Simple Adaptive Median Filter for the Removal of Impulse Noise from Highly Corrupted Images. IEEE Transactions on Consumer Electronics 2008; 54(4): 1920-7.

. Young-ll P, Rae-Hong P, Chang S. Noise Reduction in High-ISO Images Using 3-D Collaborative Filtering and Structure Extraction from Residual Blocks. IEEE Transactions on Consumer Electronics 2011; 57(2): 687-95.


Refbacks

  • There are currently no refbacks.