PERBAIKAN KUALITAS CITRA LOW-LIGHT PADA FETAL ECHOCARDIOGRAPHY MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

MUHAMMAD, RAP NUR and Sutarno, Sutarno (2021) PERBAIKAN KUALITAS CITRA LOW-LIGHT PADA FETAL ECHOCARDIOGRAPHY MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Images with low contrast (dark) and images that have unclear objects make objects in the image difficult to identify either systemically or by observers. Nowadays, almost everyone has an interest in capturing images every day using various digital devices. The quality and resolution of the captured images are important. When one captures images in low-light conditions, the images often experience low visibility. In addition to reducing the visual aesthetics of the image, this poor quality may also significantly degrade the performance of many computer vision and multimedia algorithms designed for high-quality input. Therefore, good digital devices and lighting are indispensable. For this reason, a program was created that will improve image quality with low-light image data on Fetal Echocardiography images, and use the Convolutional Neural Network (CNN) method to increase visibility on these low-light images.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Low-light, Image Enhancement, Fetal Echocardiography, Convolutional Neural Network (CNN), Computer Vision
Subjects:	T Technology > TR Photography > TR287-500 Photographic processing. Darkroom technique
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Rap Nur Muhammad
Date Deposited:	23 Mar 2022 02:01
Last Modified:	23 Mar 2022 02:01
URI:	http://repository.unsri.ac.id/id/eprint/66318

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