A new impulse noise removal technique is presented to restore digital images corrupted by impulse noise. The algorithm is based on fuzzy impulse detection technique, which can remove impulse noise efficiently from highly corrupted images while preserving image details. Extensive experimental results show that the proposed technique performs significantly better than many existing state-of-the-art algorithms. Due to its low complexity, the proposed algorithm is very suitable for hardware implementation. Therefore, it can be used to remove impulse noise in many consumer electronics products such as digital cameras and digital television (DTV) for its performance and simplicity.
"For the output of the AFSNR filter, í µí± í µí±,í µí± is the modification to include the correction terms to works presented in   . The correction term í µí± í µí±,í µí± adopted by the proposed AFSNR filter is presented in (12). "
[Show abstract][Hide abstract] ABSTRACT: Graphical abstract Abstract Noise reduction is a necessary procedure for the iris recognition systems. This paper proposes an adaptive fuzzy switching noise reduction (AFSNR) filter to reduce noise for iris pattern recognition. The proposed low complexity AFSNR filter removes noise pixels by fuzzy switching between an adaptive median filter and the filling method. The threshold values of AFSNR filter are calculated on the basis of the histogram statistics of eyelashes, pupils, eyelids, and light illumination. The experimental results on the CASIA V3.0 iris database, with genuine acceptance rate equals 99.72%, show the success of the proposed method.
"The differential rank impulse detector (DRID), presented in , implemented the impulse detector based on comparison of signal samples within a narrow rank window by both rank and absolute value. In , a simple fuzzy impulse detector (SFID) was proposed to remove the impulse noise. An alpha-trimmed mean method was presented in  which uses the alpha-trimmed mean in impulse detection and the noisy pixels predicted by a linear combination of its original value and median of its local window. "
[Show abstract][Hide abstract] ABSTRACT: This paper proposes an efficient procedure for removal of salt and pepper noises from the noisy images on the basis of their local edge preserving filters. This algorithm consists of two major stages. In the first stage, the maximum and minimum pixel value in the the corrupted image is used to select noisy pixels or noise free pixels and then in second stage, local edge preserving filters are used on the basis of noisy pixel detected and the nature of its neighboring pixels in the selected window. Comparing the obtained results with other computationally simple noise removal techniques, our proposed algorithm gives much better qualitative and quantitative performance. Due to its simplicity and low computational cost, our method is suitable for its application in many real time situations.
Image and Signal Processing and Analysis (ISPA), 2013 8th International Symposium on; 01/2013
"However, the filter smears some of the details and the edges of the original image when the noise level is over 50 . The switching approach algorithms have been introduced to protect the fine details in the image and to avoid the damage of the uncorrupted pixels . Nonlinear filters such as adaptive median filter (AMF)  can be used for discriminating corrupted and uncorrupted pixels and then apply the filtering technique. "
[Show abstract][Hide abstract] ABSTRACT: A new image denoising algorithm is proposed to restore digital images corrupted by impulse noise. It is based on two dimensional cellular automata (CA) with the help of fuzzy logic theory. The algorithm describes a local fuzzy transition rule which gives a membership value to the corrupted pixel neighborhood and assigns next state value as a central pixel value. The proposed method removes the noise effectively even at noise level as high as 90%. Extensive simulations show that the proposed algorithm provides better performance than many of the existing filters in terms of noise suppression and detail preservation. Also, qualitative and quantitative measures of the image produce better results on different images compared with the other algorithms.
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