Detail-preserving median based filters in image processing
TL;DR: A switching scheme for median filtering which is suitable to be a prefilter before some subsequent processing e.g. edge detection or data compression is presented to remove impulse noises in digital images with small signal distortion.
About: This article is published in Pattern Recognition Letters.The article was published on 1994-04-01. It has received 717 citations till now. The article focuses on the topics: Median filter & Weighted median.
Citations
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TL;DR: In this paper, a progressive switching median (PSM) filter is proposed to restore images corrupted by salt-pepper impulse noise, where an impulse detection algorithm is used before filtering, thus only a proportion of all the pixels will be filtered; and progressive methods are progressively applied through several iterations.
Abstract: A new median-based filter, progressive switching median (PSM) filter, is proposed to restore images corrupted by salt-pepper impulse noise. The algorithm is developed by the following two main points: 1) switching scheme-an impulse detection algorithm is used before filtering, thus only a proportion of all the pixels will be filtered; and 2) progressive methods-both the impulse detection and the noise filtering procedures are progressively applied through several iterations. Simulation results demonstrate that the proposed algorithm is better than traditional median-based filters and is particularly effective for the cases where the images are very highly corrupted.
1,012 citations
Cites background or methods from "Detail-preserving median based filt..."
...It should be mentioned that the impulse detection measurement used here is first introduced by Sun and Neuvo in their switch I scheme [4]....
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...free image should be locally smoothly varying, and is separated by edges [4]....
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...In the past two decades, median-based filters have attracted much attention because of their simplicity and their capability of preserving image edges [1]–[4]....
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...To determine WD and TD , we first make a rough estimation on the noise ratio, which again uses the impulse detection measurement of Sun and Neuvo’s switch I scheme [4]....
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TL;DR: A novel adaptive operator is devises, which forms estimates based on the differences between the current pixel and the outputs of center-weighted median (CWM) filters with varied center weights, which consistently works well in suppressing both types of impulses with different noise ratios.
Abstract: Previous median-based impulse detection strategies tend to work well for fixed-valued impulses but poorly for random-valued impulse noise, or vice versa. This letter devises a novel adaptive operator, which forms estimates based on the differences between the current pixel and the outputs of center-weighted median (CWM) filters with varied center weights. Extensive simulations show that the proposed scheme consistently works well in suppressing both types of impulses with different noise ratios.
741 citations
Additional excerpts
...Recently, such detection based median filtering techniques realized by thresholding operations have been investigated using differently defined impulse detectors [5]–[7]....
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TL;DR: A new impulse noise detection technique for switching median filters is presented, which is based on the minimum absolute value of four convolutions obtained using one-dimensional Laplacian operators, and is directed toward improved line preservation.
Abstract: A new impulse noise detection technique for switching median filters is presented, which is based on the minimum absolute value of four convolutions obtained using one-dimensional Laplacian operators. Extensive simulations show that the proposed filter provides better performance than many of the existing switching median filters with comparable computational complexity. In particular, the proposed filter is directed toward improved line preservation.
688 citations
TL;DR: A new decision-based algorithm is proposed for restoration of images that are highly corrupted by impulse noise that removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level.
Abstract: A new decision-based algorithm is proposed for restoration of images that are highly corrupted by impulse noise. The new algorithm shows significantly better image quality than a standard median filter (SMF), adaptive median filters (AMF), a threshold decomposition filter (TDF), cascade, and recursive nonlinear filters. The proposed method, unlike other nonlinear filters, removes only corrupted pixel by the median value or by its neighboring pixel value. As a result of this, the proposed method removes the noise effectively even at noise level as high as 90% and preserves the edges without any loss up to 80% of noise level. The proposed algorithm (PA) is tested on different images and is found to produce better results in terms of the qualitative and quantitative measures of the image
679 citations
Cites background from "Detail-preserving median based filt..."
...The filters designed for image processing are required to yield sufficient noise reduction without losing the high-frequency content of image edges [5], [8]....
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TL;DR: A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window.
Abstract: A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise.
676 citations
References
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TL;DR: The Weighted Median Filter is described, a more general filter that enables filters to be designed with a wide variety of properties and the question of finding the number of distinct ways a class of filters can act is considered and solved for some classes.
Abstract: The median filter is well-known [1, 2]. However, if a user wishes to predefine a set of feature types to remove or retain, the median filter does not necessarily satisfy the requirements. A more general filter, called the Weighted Median Filter, of which the median filter is a special case, is described. It enables filters to be designed with a wide variety of properties. Particular cases of filter requirements are discussed and the corresponding filters are derived. The notion of a minimal weighted median filter, of a subclass that act identically, is introduced and discussed. The question of finding the number of distinct ways a class of filters can act is considered and solved for some classes.
789 citations
TL;DR: In this article, the adaptive weighted median filter (AWMF) is proposed for reducing speckle noise in medical ultrasonic images. But it is not suitable for image segmentation.
Abstract: A method for reducing speckle noise in medical ultrasonic images is presented. It is called the adaptive weighted median filter (AWMF) and is based on the weighted median, which originates from the well-known median filter through the introduction of weight coefficients. By adjusting the weight coefficients and consequently the smoothing characteristics of the filter according to the local statistics around each point of the image, it is possible to suppress noise while edges and other important features are preserved. Application of the filter to several ultrasonic scans has shown that processing improves the detectability of small structures and subtle gray-scale variations without affecting the sharpness or anatomical information of the original image. Comparison with the pure median filter demonstrates the superiority of adaptive techniques over their space-invariant counterparts. Examples of processed images show that the AWMF preserves small details better than other nonlinear space-varying filters which offer equal noise reduction in uniform areas. >
715 citations
TL;DR: It is shown that optimal stack filtering under the mean-absolute-error criterion is analogous to optimal linear filtering underThe mean-squared- error criterion: both linear filters and stack filters are defined by superposition properties, both classes are implementable, and both have tractable procedures for finding the optimal filter under an appropriate error criterion.
Abstract: A method to determine the stack filter which minimizes the mean absolute error between its output and a desired signal, given noisy observations of this desired signal, is presented. Specifically, an optimal window-width-b stack filter can be determined with a linear program with O(b2/sup b/) variables. This algorithm is efficient since the number of different inputs to a window-width-b filter is M/sup b/ if the filter has M-valued input and the number of stack filters grows faster than 2 raised to the 2/sup b/2/ power. It is shown that optimal stack filtering under the mean-absolute-error criterion is analogous to optimal linear filtering under the mean-squared-error criterion: both linear filters and stack filters are defined by superposition properties, both classes are implementable, and both have tractable procedures for finding the optimal filter under an appropriate error criterion. >
227 citations
TL;DR: In this paper, an adaptive median filter is proposed, which allows the simultaneous removal of a combination of signal-dependent and additive random noise in addition to mixed impulse noise in images, processed in a single filtering pass.
Abstract: A novel adaptive median filter is proposed. It allows the simultaneous removal of a combination of signal-dependent and additive random noise in addition to mixed impulse noise in images, processed in a single filtering pass. The adaptation algorithm is based on the local signal-to-noise ratio. An extension of the class of nonlinear mean filters to adaptive filters is considered. The performance of the adaptive median filter is compared to the commonly used median filter and the nonlinear mean filter.
173 citations
TL;DR: In this paper, a new method for removing impulse noises from images is proposed, which is based on replacing the central pixel value by the generalized mean value of all pixels inside a sliding window.
Abstract: A new method for removing impulse noises from images is proposed. The filtering scheme is based on replacing the central pixel value by the generalized mean value of all pixels inside a sliding window. The concepts of thresholding and complementation which are shown to improve the performance of the generalized mean filter are introduced. The threshold is derived using a statistical theory. The actual performance of the proposed filter is compared with that of file commonly used median filter by filtering noise corrupted real images. The hardware complexity of the two types of filters are also compared indicating the advantages of the generalized mean filter.
113 citations