LUM filters: a class of rank-order-based filters for smoothing and sharpening

Hardie, Russell C.; Boncelet, Charles G.

doi:10.1109/78.205713

Cited by 156 publications

(90 citation statements)

References 28 publications

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“…Some of them are CWMF [10], [11], [17], adaptive center weighted median filter (ACWMF) [13], LUM filter [22], and SD-ROM filter [15]. In this section, we apply our algorithm to these filters and show that improved results are obtained in each case, respectively.…”

Section: Generalization To Other Rank Order Filtersmentioning

confidence: 99%

“…Table I shows the results of removing impulse noise with a random amplitude satisfying a Gaussian distribution. In the table, the second row shows the PSNR of using the standard 3 3 median filter (MF), CWMF with a filter window of 3 3 and a center weight of 5, ACWMF, LUM filter of (5, 5, 13) [22], and SD-ROM. The third row shows the results of applying our algorithm based on the corresponding filter.…”

Section: Generalization To Other Rank Order Filtersmentioning

confidence: 99%

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Adaptive Two-Pass Rank Order Filter to Remove Impulse Noise in Highly Corrupted Images

Miller

Chen³

et al. 2004

IEEE Trans. on Image Process.

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Abstract-In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.Index Terms-Center-weighted median filter (CWMF), error index matrix, impulse noise, lower-upper-middle (LUM) filter, median filter, SD-ROM filter, spatial distribution of impulse noise.

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Section: Generalization To Other Rank Order Filtersmentioning

confidence: 99%

Section: Generalization To Other Rank Order Filtersmentioning

confidence: 99%

Adaptive Two-Pass Rank Order Filter to Remove Impulse Noise in Highly Corrupted Images

Miller

Chen³

et al. 2004

IEEE Trans. on Image Process.

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“…The LUM smoothers, a subclass of rank-order based LUM filters [5,6,12] (see Figure 3) dispose by wide range of smoothing characteristics. The level of smoothing done by LUM smoother is given by tuning parameter for smoothing.…”

Section: Lum Smoothermentioning

confidence: 99%

“…However, this way is very computationally complex, and because of spatial warping and scene changes, the motion compensation often does not work well. 2D spatial filters [5,6,7] utilize the spatial correlation within the processed frame (see Figure 1b). These filters can be designed to give good filtering results without spatial blurring.…”

Section: Introductionmentioning

confidence: 99%

LUM Smoother with Smooth Control for Noisy Image Sequences

Lukáč

Marchevsky

2001

EURASIP J. Adv. Signal Process.

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This paper focuses on adaptive structure of LUM (lower-upper-middle) smoothers for noisy image sequences. For the balance between noise suppression and signal-detail preservation, the LUM smoothers are widely used in smoothing applications. The amount of smoothing done by LUM smoothers is controlled by tuning parameter. However, the smoothing level is fixed for whole image. Thus, the excessive or insufficient smoothing can be performed. This problem is solved by a new method based on the adaptive controlled level of smoothing. A new method has excellent performance of the noise reduction in the environments corrupted by the impulse noise. In addition, minimal signal-detail and motion blurring can be observed. The performance of proposed method is evaluated through objective criteria and compared with traditional temporal, spatial, and spatiotemporal LUM smoothers.

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“…If the impulse noise rate is higher than 50%, the filter blurs the base image [4]. In order to overcome this drawback, center-weighted median (CWM) filters [5], tri-state median (TSM) filters [6], and lower-upper median (LUM) filters [7] have been proposed. At a low noise density, * Correspondence: titi@erciyes.edu.tr these methods have good performances, but at a high noise density, their performances decrease.…”

Section: Introductionmentioning

confidence: 99%

Removing random-valued impulse noise in images using a neural network detector

Turkmen¹

2014

Turk J Elec Eng & Comp Sci

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This paper proposes a new method using an artificial neural network to remove random-valued impulse noise (RVIN) in images. The inputs of the neural model used to detect the RVIN are formed using basic and related gradient values. The detection of the noisy pixels is realized in 3 phases using the proposed neural detector. In order to obtain a more robust detector, 2 different networks, which are trained with an artificial training image corrupted with high and low clutter densities, are used. The extensive simulation results show that the proposed method is significantly better than the compared filters in terms of its image restoration and noise detection performance.

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LUM filters: a class of rank-order-based filters for smoothing and sharpening

Cited by 156 publications

References 28 publications

Adaptive Two-Pass Rank Order Filter to Remove Impulse Noise in Highly Corrupted Images

Adaptive Two-Pass Rank Order Filter to Remove Impulse Noise in Highly Corrupted Images

LUM Smoother with Smooth Control for Noisy Image Sequences

Removing random-valued impulse noise in images using a neural network detector

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