Removal of random-valued impulse noise by local statistics

Dawood, Hassan; Dawood, Hussain; Guo, Ping

doi:10.1007/s11042-014-2246-1

Cited by 22 publications

(7 citation statements)

References 17 publications

(57 reference statements)

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“…Simulations have been carried out in MATLAB R2013a. We evaluated the performance of the proposed method for a noise density varying from 75 to 95% RVIN and compared it with that of different filters like NAFSWM [14], ROAD [23], ROLD [21], TBLI [17], ASM [8], AFIDM [3], and CBD [9].…”

Section: Methodsmentioning

confidence: 99%

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Singh

Maheswari

2018

J Image Video Proc.

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This study presents a novel noise detection algorithm which satisfactorily detects noisy pixels in images corrupted by random-valued impulse noise of high levels up to 80% noise density. Three levels of adaptive thresholds along with an auxiliary condition are used in this method which adequately addresses the drawbacks of existing methods, especially the miss detection of noise-free pixels as noisy pixels and vice versa. A noise signature is calculated for every pixel and compared with the first threshold to identify noise followed by the comparison of the central pixel with the second and third levels of thresholds. In addition to the standard deviation and mean, the concept of quartile has been used as another measure of dispersion. After detection, a fuzzy switching weighted median filter is applied to restore the corrupted image. The simulation results demonstrate that the proposed method is able to outperform the existing methods in both the detection and filtering of random-valued impulse noise in images.

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Section: Methodsmentioning

confidence: 99%

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Singh

Maheswari

2018

J Image Video Proc.

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“…The FLS is used to determine which underutilized Wi-Fi access points should be switched off, allowing the proposed method to save energy while maintaining acceptable network performance. Hassan et al [43] presented a hybrid method based on median filters and fuzzy logic to identify and remove the salt-and-pepper impulse noise (SPN) that appears during image acquisition or transmission.…”

Section: Bcs Was Pioneered By Ganmentioning

confidence: 99%

Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks

Heng

Aimtongkham

et al. 2020

Sensors

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: The transmission of high-volume multimedia content (e.g., images) is challenging for a resource-constrained wireless multimedia sensor network (WMSN) due to energy consumption requirements. Redundant image information can be compressed using traditional compression techniques at the cost of considerable energy consumption. Fortunately, compressed sensing (CS) has been introduced as a low-complexity coding scheme for WMSNs. However, the storage and processing of CS-generated images and measurement matrices require substantial memory. Block compressed sensing (BCS) can mitigate this problem. Nevertheless, allocating a fixed sampling to all blocks is impractical since each block holds different information. Although solutions such as adaptive block compressed sensing (ABCS) exist, they lack robustness across various types of images. As a solution, we propose a holistic WMSN architecture for image transmission that performs well on diverse images by leveraging saliency and standard deviation features. A fuzzy logic system (FLS) is then used to determine the appropriate features when allocating the sampling, and each corresponding block is resized using CS. The combined FLS and BCS algorithms are implemented with smoothed projected Landweber (SPL) reconstruction to determine the convergence speed. The experiments confirm the promising performance of the proposed algorithm compared with that of conventional and state-of-the-art algorithms.

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“…After the identification of corrupted pixels, these are restored by an adaptive weighted median filter that is an enhanced form of weighted median filter. The local statistics of the sliding window was also used for the denoising of images corrupted with RVIN by defining a pre-defined threshold value at the noise identification stage (Hassan Dawood, Dawood, & Guo, 2015). The most similar pixels obtained from proposed optimal directions across the central pixel were chosen for the replacement of central identified noisy pixel.…”

Section: Literature Reviewmentioning

confidence: 99%

Detail-preserving switching algorithm for the removal of random-valued impulse noise

Azhar

Dawood

et al. 2018

J Ambient Intell Human Comput

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This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tridirectional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities.

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Removal of random-valued impulse noise by local statistics

Cited by 22 publications

References 17 publications

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

Fuzzy Adaptive-Sampling Block Compressed Sensing for Wireless Multimedia Sensor Networks

Detail-preserving switching algorithm for the removal of random-valued impulse noise

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