Impulse Noise Filtering Using Robust Pixel-Wise S-Estimate of Variance

EURASIP Journal on Advances in Signal Processing, May 2010

A novel method for impulse noise suppression in images, based on the pixel-wise S-estimator, is introduced. The S-estimator is an alternative for the well-known robust estimate of variance MAD, which does not require a location estimate and hence is more appropriate for asymmetric distributions, frequently encountered in transient regions of the image. The proposed computationally efficient modification of a robust S-estimator of variance is successfully utilized in iterative scheme for impulse noise filtering. Another novelty is that the proposed iterative algorithm has automatic stopping criteria, also based on the pixel-wise S-estimator. Performances of the proposed filter are independent of the image content or noise concentration. The proposed filter outperforms all state-of-the-art filters included in a large comparison, both objectively (in terms of PSNR and MSSIM) and subjectively.

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Impulse Noise Filtering Using Robust Pixel-Wise S-Estimate of Variance

EURASIP Journal on Advances in Signal Processing Hindawi Publishing Corporation Impulse Noise Filtering Using Robust Pixel-Wise S-Estimate of Variance Vladimir Crnojevic´ 0 Nemanja I. Petrovic´ 0 Ling Shao 0 Communications and Signal Processing Group, Department of Electrical Engineering, University of Novi Sad , 21000 Novi Sad , Serbia A novel method for impulse noise suppression in images, based on the pixel-wise S-estimator, is introduced. The S-estimator is an alternative for the well-known robust estimate of variance MAD, which does not require a location estimate and hence is more appropriate for asymmetric distributions, frequently encountered in transient regions of the image. The proposed computationally efficient modification of a robust S-estimator of variance is successfully utilized in iterative scheme for impulse noise filtering. Another novelty is that the proposed iterative algorithm has automatic stopping criteria, also based on the pixel-wise S-estimator. Performances of the proposed filter are independent of the image content or noise concentration. The proposed filter outperforms all state-of-the-art filters included in a large comparison, both objectively (in terms of PSNR and MSSIM) and subjectively. 1. Introduction Corruption by the impulse noise is a frequent problem which appears in digital images. It occurs as a consequence of transmission errors, timing problems in analog-to-digital conversion, or damaged pixel elements in image sensors [ 1 ]. Regardless of its origin, the impulse noise has two important aspects: only certain parts of the image pixels are corrupted by the noise and the intensities of contaminated pixels are significantly different from the other noise-free pixels in their neighborhoods. These properties can easily make any kind of subsequent processing, such as segmentation, edge detection, or object recognition, difficult or even impossible. Therefore, the suppression of the impulse noise is usually a required preprocessing step. The major issue in impulse noise suppression is to satisfy two opposing requests. The corrupted pixels should be filtered whereas the image details have to be preserved. This task is exceptionally difficult because even the smallest amount of noise impulses which are not detected and filtered causes significant deterioration of image quality due to the nature of impulse noise. There have been proposed a large number of filtering techniques for removal of impulse noise. The classical approach is based on using median or its modifications [ 2 ]. These space-invariant methods are applied uniformly throughout the whole image, that is, apart from the noisy pixels, they unnecessarily change the noise-free pixels and impair image details. Most of the modern impulse noise filters utilize the solution based on switching scheme [ 3 ]. The noisy pixels are detected first and filtered whereas the noise-free pixels are left intact. Thus, this approach is space variant, and it is proven to be effective in preserving image details. The impulse noise detection is usually performed by comparison of some robust statistics calculated in a local neighborhood to the corresponding fixed or adaptively calculated thresholds. A plethora of the algorithms has been developed which uses this approach, for example, switching median (SM) filter [ 3 ], three-state median (TSM) filter [ 4 ], multistate median (MSM) [ 5 ], adaptive center weighted median (ACWM) filter [ 6 ], state-dependent rankorder mean (SDROM) filter [ 7 ], progressive switching median (PSM) filter [ 8 ], conditional signal-adaptive median (CSAM) filter [ 9 ], pixel-wise MAD (PWMAD) filter [ 10 ], threshold boolean filter (TBF) [ 11 ], and so forth. The detectors of the previous filters are constructed heuristically, but it is also possible to use previous knowledge and machine learning techniques in order to find an optimal decision rule. Genetic programming is utilized in GP [ 12–14 ] filters, and neural networks are employed in improved adaptive impulsive noise suppression (IAINS) filter [ 15 ]. The other popular approach relies on fuzzy logic. Impulse noise detection in fuzzy-based techniques models ambiguities between noisy impulses and image structures in order to preserve image details [ 16–18 ]. Further enhancement of the fuzzy techniques is achieved by combining them with neural networks into neurofuzzy systems [ 19 ]. Robust statistics play a central role in impulse detection, being capable of producing correct estimates in the presence of unreliable data. The most frequently used statistics are the median and its variants such as center-weighted median. Nevertheless, robust statistics based on absolute differences are proven to be successful. The trilateral filter [ 20 ] was the first one which employed rank-ordered absolute difference (ROAD) statistics. Effective modifications are given by the rank-ordered logarithmic difference (ROLD) detector [ (...truncated)


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Vladimir Crnojević, NemanjaI Petrović. Impulse Noise Filtering Using Robust Pixel-Wise S-Estimate of Variance, EURASIP Journal on Advances in Signal Processing, 2010, pp. 830702, Volume 2010, Issue 1, DOI: 10.1155/2010/830702