Koreksi Warna Pada Citra untuk Penderita Buta Warna Menggunakan Representasi Warna LMS dan CNN

ADYANI, ADELIA PUTRI (2025) Koreksi Warna Pada Citra untuk Penderita Buta Warna Menggunakan Representasi Warna LMS dan CNN. Undergraduate thesis, UPN Veteran Jawa Timur.

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Abstract

Color blindness is a visual impairment that limits an individuals ability to accurately perceive certain colors, particularly Red, Green, or Blue. This condition can hinder daily tasks, especially when color identification is crucial. This study proposes a color correction sistem designed to enhance color perception for individuals with color vision deficiency (CVD), focusing on important visual areas within an image. The method involves converting RGB images into LMS color space, simulating types of color blindness (protanopia, deuteranopia, and tritanopia), detecting visually important regions using a saliency mask, applying color correction through a ResNet-50-based deep learning model, and performing a reverse transformation back to RGB using a CycleGAN. A total of 5.019 images were used for evaluation, and the proposed sistem achieved an average Root Mean Square (RMS) error of 0.0212. The Mean Absolute Error (MAE) ranged from 0.1541 to 0.5582 depending on the CVD type. In addition to quantitative evaluation, qualitative validation was conducted through a GUI-based user test involving 10 color blind participants. The sistem showed the highest effectiveness for deuteranopia with a color recognition accuracy of 71.666%, followed by tritanopia at 59.666% and protanopia at 46.500%. These results indicate that the proposed sistem offers significant potential in aiding individuals with CVD to better interpret color-based information, especially in visually important regions of an image. Future work may explore broader datasets and alternative deep learning architectures to further improve accuracy and adaptability.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Thesis advisor	Anggraeny, Fetty Tri	NIDN0711028201	fettyanggraeny.if@upnjatim.ac.id
Thesis advisor	Puspaningrum, Eva Yulia	NIDN0005078908	evapuspaningrum.if@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)

Divisions:

Faculty of Computer Science > Departemen of Informatics

Depositing User:

Adelia Putri Adyani

Date Deposited:

07 Jul 2025 09:04

Last Modified:

07 Jul 2025 09:04

URI:

https://repository.upnjatim.ac.id/id/eprint/39265