Method of adaptive pixel averaging for impulse noise reduction in digital images

Konieczka, Adam; Balcerek, Julian; Dąbrowski, Adam

doi:10.23919/ursi.2018.8406738

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…In particular, the following are proposed: -noise detectors for the classification and detection of noisy pixels [4,5]; -hybrid filters [6,7]; -adaptive «switching» filters [8][9][10], which did not apply median filtering for pixels without noise. The most effective is the adaptive switching median filter (ASWM) [11].…”

Section: Introductionmentioning

confidence: 99%

Conveyorized implementation of ASWM image filter on PLD

Vasylchenkov¹,

Liberg²,

Mozhaiev³

et al. 2021

TAPR

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The object of research is the adaptive switching weighted median image filter (ASWM) algorithm. This algorithm is one of the most effective in the field of impulse noise suppression. The computational complexity and algorithmic features of this adaptive nonlinear filter make it impossible to implement a filter that works in real time on modern PLD microcircuits. The most problematic areas of the algorithm are the weight coefficient estimation cycle, which has no limit on the number of iterations and contains a large number of division operations. This does not allow implementing the filter on PLDs with a sufficiently effective method. In the course of the research, the programming model of the filter in Python was used. The performance of the algorithm was assessed using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Modeling made it possible to find out empirically the number of iterations of the cycle for estimating the weight coefficients at different levels of noise density and to estimate the effect of artificial limitation of the maximum number of iterations on the filter performance. Regardless of the intensity of the noise impact, the algorithm performs less than 40 iterations of the evaluation cycle. Let’s also simulate the operation of the algorithm with different variants of the division module implementation. The paper considers the main of them and offers the most optimal in terms of the ratio of accuracy/hardware costs for implementation. Thus, a modified algorithm was proposed that does not have these disadvantages. Thanks to modifications of the algorithm, it is possible to implement a pipelined ASWM image filter on modern PLDs. The filter is synthesized for the main families of Intel PLDs. The implementation, which is not inferior in terms of SSIM and PSNR metrics to the original algorithm, requires less than 65,000 FPGA logical cells and allows filtering of monochrome images with FullHD resolution at 48 frames/s at a clock frequency of 100 MHz.

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Section: Introductionmentioning

confidence: 99%

Conveyorized implementation of ASWM image filter on PLD

Vasylchenkov¹,

Liberg²,

Mozhaiev³

et al. 2021

TAPR

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show abstract

“…However, many images that are created have some imperfections called noise [6]. Such imperfections may be caused by the incapacity and instability of imaging systems or sensors to obtain ideal images [1], or natural disruptions in the surroundings during image processing [1,7], inadequate illumination, or sensor temperature resulting in noise, or during compression and transmission [1,7,8]. Data obtained with these noises can make the data unusable or lose confidence in them [1,9].…”

Section: Introductionmentioning

confidence: 99%

“…Researchers have proposed several changes or enhancements to the Standard Median Filter (SMF) due to the limitations of the SMF filter [24,30,31]. Various advanced filters have been proposed by integrating various techniques [8]. However, the SMF drawback is that the filter is applied to all pixels in the image evenly, to the noisy pixels and noise-free pixels, which leads to original details lost.…”

Section: Introductionmentioning

confidence: 99%

Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

Mursal¹,

Ibrahim²

2020

Electronics

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It is essential to restore digital images corrupted by noise to make them more useful. Many approaches have been proposed to restore images affected by fixed value impulse noise, but they still do not perform well at high noise density. This paper presents a new method to improve the detection and removal of fixed value impulse noise from digital images. The proposed method consists of two stages. The first stage is the noise detection stage, where the difference values between the pixels and their surrounding pixels are computed to decide whether they are noisy pixels or not. The second stage is the image denoising stage. In this stage, the original intensity value of the noisy pixels is estimated using only their first-order and second-order neighborhood pixels. These neighboring orders are based on the Euclidean distance between the noisy pixel and its neighboring pixels. The proposed method was evaluated by comparing it with some of the recent methods using 50 images at 18 noise densities. The experimental results confirm that the proposed method outperforms the existing filters, excelling in noise removal capability with structure and edge information preservation.

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Interest Point Detection at Digital Image Based on Averaging of Function

Yurchuk,

Kosovan

2023

Lecture Notes on Data Engineering and Communications Technologies

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Method of adaptive pixel averaging for impulse noise reduction in digital images

Cited by 5 publications

References 15 publications

Conveyorized implementation of ASWM image filter on PLD

Conveyorized implementation of ASWM image filter on PLD

Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

Interest Point Detection at Digital Image Based on Averaging of Function

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