An Adaptive Weighted Min-Mid-Max Value Based Filter for Eliminating High Density Impulsive Noise

Sharma, Nikhil; Sohi, Prateek Jeet Singh; Garg, Bharat

doi:10.1007/s11277-021-08314-5

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…However, when “ maxValue ” was set to 57 to 255, some noise appeared, and reasonable and acceptable results were found. After several trials, only when “ maxValue ” was set to 83 to 93, appropriate sharpness and clear contours were obtainable for analysis [ 19 ]. Consequently, a “ maxValue ” of 85, which showed the most stable appearance regardless of the BA was selected ( Figure 5 ).…”

Section: General Proceduresmentioning

confidence: 99%

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

Qiu,

Tsai,

Chen

et al. 2024

Sensors

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An integrated automatic optical inspection (iAOI) system with a procedure was proposed for a printed circuit board (PCB) production line, in which pattern distortions and performance deviations appear with process variations. The iAOI system was demonstrated in a module comprising a camera and lens, showing improved supportiveness for commercially available hardware. The iAOI procedure was realized in a serial workflow of image registration, threshold setting, image gradient, marker alignment, and geometric transformation; furthermore, five operations with numerous functions were prepared for image processing. In addition to the system and procedure, a graphical user interface (GUI) that displays sequential image operation results with analyzed characteristics was established for simplicity. To demonstrate its effectiveness, self-complementary Archimedean spiral antenna (SCASA) samples fabricated via standard PCB fabrication and intentional pattern distortions were demonstrated. The results indicated that, compared with other existing methods, the proposed iAOI system and procedure provide unified and standard operations with efficiency, which result in scientific and unambiguous judgments on pattern quality. Furthermore, we showed that when an appropriate artificial intelligence model is ready, the electromagnetic characteristic projection for SCASAs can be simply obtained through the GUI.

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Section: General Proceduresmentioning

confidence: 99%

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

Qiu,

Tsai,

Chen

et al. 2024

Sensors

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show abstract

“…Lic et al [20] eliminated impulsive noise using a logarithmic image prior constraint on the goal function. Sharma et al [21] developed a filter that demonstrated exceptional performance in terms of quantitative measurements and visual representation at high noise concentrations. Qaio et al [22] devised the relativistic impulse approximation (RIA) approach for the atomic ionization process generated by millicharged particles.…”

Section: Introductionmentioning

confidence: 99%

Removal of High Density Impulse Noise Using Adaptive Pulse Coupled Neural Network (APCNN) with Improved Alpha Guided Gray Wolf Optimization (IAgGWO) Technique in Transform Domain

Raja¹,

Moorthi²,

Pitchai³

2021

Wireless Pers Commun

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At lower noise levels, the majority of filter-based impulse noise removal approaches outperform each other. The purpose of this paper is to design an efficient adaptive pulse coupled neural network (APCNN) technique with improved alpha guided grey wolf optimization (IAgGWO) for the elimination of high-density impulse noise. This noise reduction technique is divided into two stages: the detection of noisy pixels and the replacement of a noisy pixel with a data pixel. The IAgGWO technique is utilised to isolate the optimal values for identifying impulse noisy pixels, and the APCNN filtering technique is used to supplant them. This technique provides more accurate and clean filtered images while preserving critical edge pixel information. To demonstrate the IAgGWO-APCNN strategy's efficacy, various degrees of impulse noise were applied to the image and tested. With PSNR of 42 percent, SSIM of 99 percentand STD of 40 percent on satellite pictures, the suggested noise removal model has proved its unshakable consistency in terms of both qualitative and quantitative assessment.

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A Tri-valued Trimmed Mean Decision-Based Filter for Removal of Salt-and-Pepper Noise

Sethi

Garg

Yadav

2022

Artificial Intelligence in Data and Big Data Processing

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An Adaptive Weighted Min-Mid-Max Value Based Filter for Eliminating High Density Impulsive Noise

Cited by 7 publications

References 22 publications

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

Integrated Automatic Optical Inspection and Image Processing Procedure for Smart Sensing in Production Lines

Removal of High Density Impulse Noise Using Adaptive Pulse Coupled Neural Network (APCNN) with Improved Alpha Guided Gray Wolf Optimization (IAgGWO) Technique in Transform Domain

A Tri-valued Trimmed Mean Decision-Based Filter for Removal of Salt-and-Pepper Noise

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