Image Enhancement Based On Bee Colony Algorithm

Chyan, Phie

doi:10.36478/jeasci.2019.43.49

Cited by 6 publications

(5 citation statements)

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“…Pemulihan gambar dimaksudkan untuk mendapatkan citra asli dari bentuknya yang mengalami degradasi. Degradasi dapat terjadi selama pembentukan citra, transmisi, dan penyimpanan serta dapat berada dalam berbagai bentuk termasuk adanya derau tambahan [6], invarian area atau varian blur, aliasing, dan kompresi secara keseluruhan. Teknik restorasi citra sebagian besar digunakan dalam sejumlah aplikasi, yaitu pencitraan medis, pencitraan astronomi, penginderaan jauh, pencitraan mikroskop, deblurring fotografi, ilmu forensik dan banyak lagi.…”

Section: Pendahuluanunclassified

Pemulihan Citra Berbasis Metode Markov Random Field

Chyan

Saptadi²

2022

Jur. Ris. Kom.

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Image processing and computer vision today are faced with increasing big data applications. Excessive collection of Image data sometimes can have bad quality due to errors at the time of acquisition or at the time of transmission, so for that problem the method is needed to perform image restoration. Image restoration is a process to make improvements to the image with the aim of obtaining a clean image from noise like the original image. Among the methods that can be used in image restoration, Markov Random Field (MRF) based on a probabilistic representation of image processing problems, namely maximizing the probability size calculated starting from the input data for all candidate solutions can provide a faster sub-optimal solution for image restoration. Based on the implementation this experiment conducted with the noisy test image, the MRF method was capable to improve the noisy image up to 96.75 percent close to the original image without noise

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Section: Pendahuluanunclassified

Pemulihan Citra Berbasis Metode Markov Random Field

Chyan

Saptadi²

2022

Jur. Ris. Kom.

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“…The proposed decision support system model consists of several subsystems, the first is a data management subsystem that utilizes structured data sources as a relational database model, the second is a management subsystem model that utilizes the developed analytic models [11,12]. This analytic model would provide to system an analytical capability by utilizing a mathematical formula to generate recommendations for the user.…”

Section: System Modellingmentioning

confidence: 99%

Design of Purebred Dog Recommendation System Using MCDM Approach

Chyan¹

2020

Adv. sci. technol. eng. syst. j.

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The dog is one of the first animals domesticated by human, and for thousands of years, it has been artificially bred into hundreds of types in order to provide certain traits that humans want. Nowadays, the selection of dogs by potential adopters has become a problem due to the availability of different type of breeds with their physical and mental characteristics. This study aims to design a decision support system through an analytical model that uses variety of data on the characteristics of purebred dogs obtained from different sources. Data from official sources is obtained from international purebred dog organizations that set the standards for each breed type, while data from unofficial sources is obtained from the dog lovers community, experts, and kennel owners. The result of the study provides appropriate recommendations to potential adopters in selecting a breed that suits their preferences and needs.

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“…The pre-processing process needs to be done to get the CCTV image that comes from the CCTV video recording frame. Algorithm Image Enhancement applied to the application is Convolution (which consists of Low Pass Filters, High Pass Filters, Median Filters, Mean Filters and Gaussian Filters) [6], [7], [8], [1], [9] Histogram Equalization [10], [11], [12][12], [13], Wiener Filter [14], Stretching Contrast [15], [10], [16], [17], [18] and Linear Interpolation [19], [20] All algorithms are collected in a program to process image quality enhancements. This image quality enhancement application is created using MATLAB.…”

Section: Introductionmentioning

confidence: 99%

“…some of them try to repair digital video [21], improve the screenshot results [20] and mammogram [22]. This study only compares the performance of Image Enhancement algorithms with other algorithms such as Histogram Equalization [10], [11], [12], Stretching Contrast [15], [10], [18], Convolution [6], [7], [12], , Wiener Filter [14] and Interpolation [19] [20].…”

Section: Introductionmentioning

confidence: 99%

“…Some of them tried to repair digital video [21], improve the screenshots of images [20] and mammogram [22]. Some of them only compare the JOIN (Jurnal Online Informatika) ISSN 2527-1682 (Print) ISSN 2527-9165 (Online) Implementation of Image Enhancement Algorithm for Image Forensics Using Matlab (Fauzan Novaldi Suteja, Eka Wahyu Hidayat, Nur Widiyasono) performance of the Image Enhancement algorithms with other algorithms such as Histogram Equalization [10], [11], [12], Stretching Contrast [15], [10], [16], [18], Convolution [6], [7], [12], [1], Wiener Filter [14] and Interpolation [19] [20].…”

Section: Introductionmentioning

confidence: 99%

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Implementation of Image Enhancement Algorithm for Image Forensics using Mathlab

Suteja

Hidayat

Widiyasono

2020

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The purpose of this journal is to explain the implementation of the image enhancement algorithm for image forensics. Image Forensic deals with the types of digital evidence in the form of digital image files. One of the most commonly used digital devices in providing digital evidence for forensic analysis is CCTV (Closed-Circuit Television). CCTV images have a low quality such as noise, blur, lack of light intensity, etc., so that the image must be enhanced so that forensic analysis can be done. To enhance image quality, an application is needed by applying the image enhancement algorithm. The algorithm applied to the application is a Low Pass Filter to increase low pixel intensity, High Pass Filter to increase high pixel intensity, Median Filter to replaces the original pixel value with the pixel center value of the image, Mean Filter to replaces the original pixel value with a value the average pixel of the image, the Gaussian Filter for reducing noise in the image, the Wiener Filter to reduce blur in the image, the Histogram Equalization spreads the image histogram value, Contrast Stretching to stretch the contrast intensity in the image and Bicubic Interpolation to increase the image size and resize the image. In this study, the application was built using MATLAB and the testing process for each algorithm was based on Timing-Run, MSE and PSNR parameters. From the test, the average MSE value is 1058.512083 and the PSNR value is 541.61875 dB, which means that the resulting image has a fairly high level of similarity and the average time needed to process the algorithm for the image is 0.114627915 seconds.

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Image Enhancement Based On Bee Colony Algorithm

Cited by 6 publications

References 0 publications

Pemulihan Citra Berbasis Metode Markov Random Field

Pemulihan Citra Berbasis Metode Markov Random Field

Design of Purebred Dog Recommendation System Using MCDM Approach

Implementation of Image Enhancement Algorithm for Image Forensics using Mathlab

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