An improved Directional Weighted Median Filter for restoration of images corrupted with high density impulse noise

Shukla, Awanish Kumar; Bhateja, Vikrant; Verma, Rishendra; Alam, Mohd Sanawer

doi:10.1109/icroit.2014.6798376

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…Due to which lot of image details vanishes away from the noise free pixels as well. Due to this problem, many variants of median based filter [5,6,7,8,9,10] and filters employing soft-computing or other strategies [11,12,13] has been proposed. Performance improvement has been seen in these filters but at the cost of increase in complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

Impulse Noise Detection and Removal Using First Order and Second Order Difference with Multistage Directional Statistics

Rasheed

Hussain

2014

2014 12th International Conference on Frontiers of Information Technology

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Most of the image processing techniques does not perform well when images are corrupted with noise therefore restoration of corrupted images is considered as preprocessing step in most of the image processing applications. Detection and removal of impulse noise is an active area of research and in this paper a novel technique has been proposed which remove Random value impulse noise (RVIN) from digital gray scale images. The proposed method consists of two main modules. First module uses directional based statistics to detect noisy pixels. Detected noisy pixels are passed to the filtering scheme for estimation of their noise free values. Proposed technique has been compared using well known performance measure peak-signalto-noise ratio. Simulations show that the proposed filter can provide excellent performance of suppressing impulse noise in all situations.

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Section: Literature Reviewmentioning

confidence: 99%

Impulse Noise Detection and Removal Using First Order and Second Order Difference with Multistage Directional Statistics

Rasheed

Hussain

2014

2014 12th International Conference on Frontiers of Information Technology

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“…The fundament of median filter is to use mid-value in a neighbourhood of a point to replace the point. This method is capable of eliminating isolated noise (Omura et al , 2013; Shukla et al , 2014; Vijaykumar et al , 2014). The mid-value of the sample X ( n − N ) · · · X ( n ) · · · X ( n + N ) is selected in the scanning area of LIDAR.…”

Section: Obstacles Detectionmentioning

confidence: 99%

Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

Xie

Liu

et al. 2017

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Purpose Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance. Design/methodology/approach The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer. Findings The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials. Originality/value The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

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“…However, assuming that high-density noise exists in a grayscale image, it is difficult to judge whether a pixel is noise by ranking median values. This may generate a poor denoising effect in the case of processing high-noise grayscale images [22].…”

Section: Introductionmentioning

confidence: 99%

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

Cao,

Liu

2024

Applied Sciences

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Grayscale image processing is a key research area in the field of computer vision and image analysis, where image quality and visualization effects may be seriously damaged by high-density salt and pepper noise. A traditional median filter for noise removal may result in poor detail reservation performance under strong noise and the judgment performance of different noise characteristics has strong dependence and rather weak robustness. In order to reduce the effects of high-density salt and pepper noise on image quality when processing high-noise grayscale images, an improved two-dimensional maximum Shannon entropy median filter (TSETMF) is proposed for the adaptive selection of a threshold to enhance the filter performance while stably and effectively retaining the details of the images. The framework of the proposed improved TSETMF algorithm is designed in detail. The noise in images is filtered by means of automatically partitioning a window size, the threshold value of which is adaptively calculated using two-dimensional maximum Shannon entropy. The theoretical model is verified and analyzed through comparative experiments using three kinds of classical grayscale images. The experimental results demonstrate that the proposed improved TSETMF algorithm exhibits better processing performance than that of the traditional filter, with a higher suppression of high-density noise and denoising stability. This stronger ability while processing high-density noise is demonstrated by a higher peak signal-to-noise ratio (PSNR) of 24.97 dB with a 95% noise density located in the classical Lena grayscale image. The better denoising stability, with a noise density from 5% to 95%, is demonstrated by the minor decline in the PSNR of approximately 10.78% relative to a PSNR of 23.10 dB located in the classical Cameraman grayscale image. Furthermore, it can be advanced to promote higher noise filtering and stability for processing high-density salt and pepper noise in grayscale images.

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An improved Directional Weighted Median Filter for restoration of images corrupted with high density impulse noise

Cited by 16 publications

References 31 publications

Impulse Noise Detection and Removal Using First Order and Second Order Difference with Multistage Directional Statistics

Impulse Noise Detection and Removal Using First Order and Second Order Difference with Multistage Directional Statistics

Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

High-Noise Grayscale Image Denoising Using an Improved Median Filter for the Adaptive Selection of a Threshold

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