Review Study of Image De-Noising on Digital Image Processing and Applications

Abdulah, C. S. K.; Rohani, Mohamad Nur Khairul Hafizi; Ismail, B.; Isa, Muzamir; Rosmi, A. S.; Mustafa, Wan Azani; Abdullah, Andi Zulkifli; Ariffin, Wan Nor Munirah; Jamil, Mohamad Kamarol Mohd

doi:10.37934/araset.30.1.331343

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…The pre-processing stage is important in modifying the raw image to be use in the next stage [14]. It is a simple process of modifying the pixel brightness of an image.…”

Section: Image Processingmentioning

confidence: 99%

“…( 12) with the value of r = 2. (14) Therefore, the second centroid surrounding neighbor can be denoted as: (15) Subsequently, for the value of the third centroid, the procedure is reiterate by obtaining the centroid between the data samples and the previous nearest centroid neighbors. A new equation derived from Eq.…”

Section: Fig 6 Membership Function Partitioningmentioning

confidence: 99%

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An Evaluation of Fuzzy in Image Enhancement: Design and Comparison for Penicillium and Aspergillus Species

Farah Nabilah Zabani,

Haryati Jaafar,

Nur Rodiatul Raudah Mohamed Radzuan

et al. 2024

ARASET

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The main focus in this study is to enhance and classify the image of a type of filamentous fungi named Penicillium and Aspergillus. For image enhancement, fuzzy-partition gamma adaptive histogram equalization (FpGAHE) is proposed to improve the quality of an image, in particular the low quality of a microscopic image. Two stages have been considered in this technique. In the first stage, a fuzzy partition is developed to handle the inconsistency of the gray level values of the images by introducing a fuzzy set. In the second stage, surrounding neighborhood is employed to avoid the imbalance data and reduce the drastically changes of brightness values of the image. The performances are evaluated into two parts i.e., image processing and image classification by using the collected microscopic images of fungi species. To evaluate the effectiveness of the proposed technique, the existing techniques, AHE and AGC is compared to the FpGAHE. In image processing, the result attained shows that the proposed technique has a better performance by obtaining the highest value for the PSNR, SSIM and FSIM evaluation for the species of A. terreus in clean condition. Meanwhile, in image classification, five different nearest neighbor classifiers have been tested. The results show the proposed FpGAHE with Improved Fuzzy-Based k Nearest Centroid Neighbor (IFkNCN) classifier perform the best result compare to other nearest neighbor classifier by obtaining the value of 92.59 and 93.95 for the salt and pepper and Gaussian noise corrupted images respectively.

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“…The pre-processing stage is important in modifying the raw image to be use in the next stage [14]. It is a simple process of modifying the pixel brightness of an image.…”

Section: Image Processingmentioning

confidence: 99%

Section: Fig 6 Membership Function Partitioningmentioning

confidence: 99%

An Evaluation of Fuzzy in Image Enhancement: Design and Comparison for Penicillium and Aspergillus Species

Farah Nabilah Zabani,

Haryati Jaafar,

Nur Rodiatul Raudah Mohamed Radzuan

et al. 2024

ARASET

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show abstract

“…Image processing allows a far greater choice of procedures to be applied to the input data, avoiding concerns such as noise build-up and signal distortion. Before beginning with the de-noising process, several forms of noise such as Gaussian noise, salt and pepper noise, speckle noise, and poison noise are commonly employed to improve the image [25].…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid of NHPP and Generalized Gaussian Mixture Model: A Combinatorial Approach for Background Elimination

Ch Lavanya Ratna,

Y Srinivas

2023

ARASET

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For background removal, a software reliability model based on NHPP's combinatorial method and parametric modeling using the Chimp Optimization Algorithm (ChOA) and Generalized Gaussian Mixture Model is proposed in this paper. A novel combination of ChOA and GGMM algorithms with the NHPP strategy is devised to solve the major shortcomings of the original algorithms. The model is examined using the provided data set (COCO 2017). This methodology demonstrates how the suggested work more successfully recognizes background photos. The evaluation of the results is done by using several metrics like Accuracy, Recall, Precision, F-Score, Peak Signal Noise Ratio, and Mean Square Error. The outcomes are evaluated against several models based on frame differences, adaptive background removal, adaptive frame differences, and the Gaussian mixture model.

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Influence of Electrical Tree Characterization Change Based on Tree Inception Voltage in HV Solid Insulation

Abdulah,

Rosmi,

Isa

et al. 2023

2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC)

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Review Study of Image De-Noising on Digital Image Processing and Applications

Cited by 3 publications

References 28 publications

An Evaluation of Fuzzy in Image Enhancement: Design and Comparison for Penicillium and Aspergillus Species

An Evaluation of Fuzzy in Image Enhancement: Design and Comparison for Penicillium and Aspergillus Species

A Hybrid of NHPP and Generalized Gaussian Mixture Model: A Combinatorial Approach for Background Elimination

Influence of Electrical Tree Characterization Change Based on Tree Inception Voltage in HV Solid Insulation

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