Abstract

Increasing public concern about security due to the lack of CCTV monitoring and lighting at night. The problem is that the quality of CCTV is low if the room does not have normal lighting or even tends to be dark. Images produced from cameras that have poor performance produce dark images so that the resulting information cannot be used for the next process. In this research, the image improvement results of the MIRNet and Zero-DCE methods were compared, from this comparison the best method was obtained. To improve the quality of low/dark images, deep learning-based approaches such as MIRNet and Zero-Deep Curve Estimation (Zero-DCE) have received great attention. This study aims to compare the performance of both approaches in the context of image enhancement in low-light conditions. This research uses Low-Light (LoL) and real CCTV datasets. The data is divided into training and testing data. Then the data was analyzed using MIRNet and Zero-DCE. The test results using MIRNet have a PSNR of 27.86 dB and an MSE of 10.595. Meanwhile, Zero-DCE has average PSNR and MSE values of 28.055 dB and 10.204. The PSNR and MSE results show that Zero-DCE has the best average results between the two existing methods.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages290-295
Number of pages6
ISBN (Electronic)9798350382266
DOIs
Publication statusPublished - 2023
Event7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023 - Purwokerto, Indonesia
Duration: 29 Nov 202330 Nov 2023

Publication series

NameProceedings - 2023 IEEE 7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023

Conference

Conference7th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2023
Country/TerritoryIndonesia
CityPurwokerto
Period29/11/2330/11/23

Keywords

  • Low-Light dataset
  • MIRNet
  • MSE
  • PSNR
  • Zero-DCE
  • image enhancement

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