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Inyasdi Kahvi, Muhamad Restu
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Agriculture, Biological Sciences & Forestry Computer Science & IT

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Journal : Jurnal Ilmu Komputer dan Agri-Informatika

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Perbaikan Kualitas Citra Cahaya Redup Menggunakan Teknik Perbaikan Histogram Equalization dan Adaptive Multi-scale Retinex Muhammad, Fadhel; Aprilianti, Dhila; Nelvi, Annisa Amanda; Khairunisa, Aulia; Inyasdi Kahvi, Muhamad Restu; Giri, Endang Purnama; Marcelita, Faldiena
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 11 No. 1 (2024)
Publisher : Departemen Ilmu Komputer, Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.11.1.19-26

Abstract

The low-light images often have low quality, with insufficient light causing dark images, low contrast, and neglected details. To enhance low-light images, various methods have been developed, including histogram equalization and adaptive multi-scale retinex (AMSR). Among these methods, there is no consensus on which method is more effective in improving low-light images. In this study, we compare the performance of histogram equalization and AMSR methods in enhancing low-light images. The histogram equalization method is applied to alter the pixel intensity distribution within the image. Histogram equalization has drawbacks in maintaining local contrast and can produce overly sharp images. Furthermore, the AMSR method is applied to improve the contrast and detail of low-light images. This study applies AMSR with adaptive scales at various detection levels. The results show that both methods can enhance low-light images. The histogram equalization method provides a significant improvement in global contrast and image brightness, while the AMSR method successfully maintains local contrast and image detail. Differences also occur in the obtained results, depending on the image characteristics and user preferences. Based on the analysis and evaluation conducted, it can be concluded that both methods have their respective strengths and weaknesses in improving low-light images.
Co-Authors Aprilianti, Dhila Endang Purnama Giri Khairunisa, Aulia Marcelita, Faldiena Muhammad, Fadhel Nelvi, Annisa Amanda