Image Enhancement Pada Screen Capture CCTV Dengan Menggunakan Metode Histogram EKualisasi

Nugroho, Hendro

doi:10.22219/kinetik.v2i2.155

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…Berdasarkan penelitian sebelumnya telah dilakukan perbaikan citra pada hasil pembesaran dengan kualitas yang terbaik dengan menggunakan metode Interpolasi fractal untuk perbaikan citra pembesaran Nilai skala vertikal 0 dapat menghasilkan kualitas citra pembesaran yang terbaik, semakin nilai vertikal nya menjauh dari 0 maka semakin rendah kualitas citra yang di hasilkan [1]. Melakukan penelitian dengan menggunakan metode Histogram Ekualisasi hasil dari Histogram Ekualisasi ini dilakukan image enhancement yang terdapat peningkatan pada nilai rata-rata intensitas pada citra Screen Capture CCTV yang dapat mengakibatkan citra uji coba 1 samapai citra uji coba 4 intensitas cahayanya menyebar dan untuk kontras citra ini juga mengalami peningkatan nilai kecerahan pada hasil citra Histogram Ekualisasi [2]. Melakukan penelitian menggunakan metode interpolasi linear pengubahan ukuran untuk menerapkan pembesaran resolusi citra sehingga ukuran citra lebih besar dari aslinya dengan menyisipkan titik diantara dua titik masukan yang telah tepat posisinya, data gambar yang berbentuk matrik dua dimensi ini dan dapat disimpan secara efektif dalam variabel yang bertipe larik [3].…”

Section: Pendahuluanunclassified

Implementasi Interpolasi Fractal Untuk Pembesaran Skala Pada Citra Screen Capture CCTV

Hasanah¹

2020

Jur. Ris. Kom.

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Various types of image processing that can be done by computers today such as image enlargement, image quality improvement and sharpening of an image are one of the fields that are quite popular today. Sharpening an image is very necessary so that someone can see and observe clearly the object of the image and the absence of interference. An image can have damage to some parts or blocks of pixels in the sending or storage process. This damage is the main form of error in the image, for example the image of CCTV Capture. The problem that is often faced when we want to see a CCTV recording is the obscurity of an object image that we want to see. If the Screen Capture and zoom in will be increasingly unclear the image object. The results of this Screen Capture image have a better level of sharpness if it is enlarged directly, the Screen Capture is usually used as evidence and so on. This image improvement is done by the operation process of the selection point of the results of the CCTV Screen Capture image that is used as a sample. After the Screen Capture image selection results are obtained then the image is in Grayscale to make a uniform pixel value, the next process is the process of enlarging the image of the Screen Capture image by adding new pixels. This is done by inserting the new pixel points between the points where the pixel value has been set directly from the original image. To overcome these problems requires the Fractal Interpolation method to improve image quality enhancement. With this method it is expected to be able to solve the problem of improving the image quality enlargement of the results of the CCTV Capture Screen. This research was conducted to implement the process of improving image quality magnification based on the Fractal Interpolation method.

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Section: Pendahuluanunclassified

Implementasi Interpolasi Fractal Untuk Pembesaran Skala Pada Citra Screen Capture CCTV

Hasanah¹

2020

Jur. Ris. Kom.

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“…Each array value in an image is called a pixel [1][2][3]. The number of pixels in an image will determine the image's quality, the more pixels in an image, the better the image [4,5]. The image's information is divided to be two-part, that is 2D images and 3D images [2,3].…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction using convolution smooth method

et al. 2021

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3D imagery is an image with depth data. The use of depth information in 3D images still has many drawbacks, especially in the image results. Raw data on the 3D camera even does not look smooth, and there is too much noise. Noise in the 3D image is in the form of imprecise data, which results in a rough image. This research will use the convolution smooth methods to improve the 3D image. Will smooth noise in the 3D image, so the resulting image will be better. This smoothing system is called the blurring effect. This research has been tested on flat objects and objects with a circle contour. The test results on the flat surface obtained a distance of 1.3177, the test in the object with a flat surface obtained a distance of 0.4937, and the test in circle contour obtained a distance of 0.3986. This research found that the 3D image will be better after applying the convolution smooth method.

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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%

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 Pada Screen Capture CCTV Dengan Menggunakan Metode Histogram EKualisasi

Cited by 5 publications

References 8 publications

Implementasi Interpolasi Fractal Untuk Pembesaran Skala Pada Citra Screen Capture CCTV

Implementasi Interpolasi Fractal Untuk Pembesaran Skala Pada Citra Screen Capture CCTV

3D Reconstruction using convolution smooth method

Implementation of Image Enhancement Algorithm for Image Forensics using Mathlab

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