An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Chen, Fengyu; Huang, Minghui; Ma, Zhuxi; Li, Yibo; Huang, Qianbin

doi:10.3390/sym12121990

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…Impulsive filtering: This class primarily focuses on nonlinear filters and is commonly addressed through median-based filters, as in [ 18 ], where a fuzzy paradigm was also applied. Similarly, iterative mean/median filters were used in the works of Chen [ 19 , 20 ], and robust statistical methods that achieve remarkable results at rejecting atypical data [ 21 ], which is a distinctive characteristic of impulsive noise. These filters typically perform well with high noise densities but are limited to only one type of noise, and their computational costs can be exhaustive due to their iterative processing.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators

Rendón-Castro

Mújica-Vargas

Luna-Álvarez

et al. 2023

Entropy

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In the field of image processing, noise represents an unwanted component that can occur during signal acquisition, transmission, and storage. In this paper, we introduce an efficient method that incorporates redescending M-estimators within the framework of Wiener estimation. The proposed approach effectively suppresses impulsive, additive, and multiplicative noise across varied densities. Our proposed filter operates on both grayscale and color images; it uses local information obtained from the Wiener filter and robust outlier rejection based on Insha and Hampel’s tripartite redescending influence functions. The effectiveness of the proposed method is verified through qualitative and quantitative results, using metrics such as PSNR, MAE, and SSIM.

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Section: Related Workmentioning

confidence: 99%

Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators

Rendón-Castro

Mújica-Vargas

Luna-Álvarez

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…In the past, denoising algorithms typically used pre-defined noise models. These models are mathematical tools designed to represent common noise types in digital images, like Gaussian noise, salt-and-pepper noise, and speckle noise [9]. They work best when the noise's origin and characteristics are known, the noise remains uniform, and external factors introducing the noise can be predicted or controlled.…”

Section: Introductionmentioning

confidence: 99%

Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

Rahman,

Aamir,

Bhutto

et al. 2023

Symmetry

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The distribution of real noise in images can disrupt the inherent symmetry present in many natural visuals, thus making its effective removal a paramount challenge. However, traditional denoising methods often require tedious manual parameter tuning, and a significant portion of deep learning-driven techniques have proven inadequate for real noise. Moreover, the efficiency of end-to-end algorithms in restoring symmetrical patterns in noisy images remains questionable. To harness the principles of symmetry for improved denoising, we introduce a dual deep learning model with a focus on preserving and leveraging symmetrical patterns in real images. Our methodology operates in two stages. In the first, we estimate the noise level using a four-layer neural network, thereby aiming to capture the underlying symmetrical structures of the original image. To enhance the extraction of symmetrical features and overall network performance, a dual attention mechanism is employed before the final convolutional layer. This innovative module adaptively assigns weights to features across different channels, thus emphasizing symmetry-preserving elements. The subsequent phase is devoted to non-blind denoising. It integrates the estimated noise level and the original image, thus targeting the challenge of denoising while preserving symmetrical patterns. Here, a multi-scale architecture is used, thereby amalgamating image features into two branches. The first branch taps into dilation convolution, thus amplifying the receptive field without introducing new parameters and making it particularly adept at capturing broad symmetrical structures. In contrast, the second branch employs a standard convolutional layer to focus on finer symmetrical details. By harnessing varied receptive fields, our method can recognize and restore image symmetries across different scales. Crucial skip connections are embedded within this multi-scale setup, thus ensuring that symmetrical image data is retained as the network deepens. Experimental evaluations, conducted on four benchmark training sets and 12 test datasets, juxtaposed with over 20 contemporary models based on the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics, underscore our model’s prowess in not only denoising but also in preserving and accentuating symmetrical elements, thereby setting a new gold standard in the field.

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“…Although these typical filters are of lightweight calculation, they are extremely interfered with by noise. Correspondingly, the improved filter methods of median [ 13 , 14 ], mean [ 15 ] and Gaussian [ 16 ] were proposed to effectively suppress clutters. In addition, some improved morphological methods such as Top-Hat [ 17 ] and region grow [ 18 ] were studied.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor

Zhang

et al. 2021

Sensors

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Due to the complexity of background and diversity of small targets, robust detection of infrared small targets for the trajectory correction fuze has become a challenge. To solve this problem, different from the traditional method, a state-of-the-art detection method based on density-distance space is proposed to apply to the trajectory correction fuze. First, parameters of the infrared image sensor on the fuze are calculated to set the boundary limitations for the target detection method. Second, the density-distance space method is proposed to detect the candidate targets. Finally, the adaptive pixel growth (APG) algorithm is used to suppress the clutter so as to detect the real targets. Three experiments, including equivalent detection, simulation and hardware-in-loop, were implemented to verify the effectiveness of this method. Results illustrated that the infrared image sensor on the fuze has a stable field of view under rotation of the projectile, and could clearly observe the infrared small target. The proposed method has superior anti-noise, different size target detection, multi-target detection and various clutter suppression capability. Compared with six novel algorithms, our algorithm shows a perfect detection performance and acceptable time consumption.

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An Iterative Weighted-Mean Filter for Removal of High-Density Salt-and-Pepper Noise

Cited by 14 publications

References 31 publications

Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators

Enhancing Image Quality via Robust Noise Filtering Using Redescending M-Estimators

Innovative Dual-Stage Blind Noise Reduction in Real-World Images Using Multi-Scale Convolutions and Dual Attention Mechanisms

Infrared Small Target Detection Method with Trajectory Correction Fuze Based on Infrared Image Sensor

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