LoLi-IEA: low-light image enhancement algorithm

Ezequiel Perez-Zarate; Oscar Ramos-Soto; Erick Rodríguez-Esparza; German Aguilar

doi:10.1117/12.2677422

4 October 2023 LoLi-IEA: low-light image enhancement algorithm

Ezequiel Perez-Zarate, Oscar Ramos-Soto, Erick Rodríguez-Esparza, German Aguilar

Proceedings Volume 12675, Applications of Machine Learning 2023; 1267512 (2023) https://doi.org/10.1117/12.2677422
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

Low-light image enhancement has posed a significant challenge in recent years due to non-uniform luminance in real-world images. Color restoration, luminance mapping, and estimation of the curve and levels are some techniques used by the algorithms to enhance the image. However, real-world images often have non-uniform luminance, requiring local enhancement in certain areas rather than global enhancement. In order to tackle this issue, this paper introduces a novel methodology based on deep learning, employing two convolutional network architectures. The first one classifies the brightness level of the input image, while the second one enhances the brightness level based on information obtained through the first architecture. To train this model, two commonly used datasets in state-of-the-art research are used: the LOL (Low-Light) and Synthetic Low-light. Both datasets contain low-light and ground-truth image pairs, which makes it possible to make a proper estimate between non-uniform and uniform luminosity. The proposed algorithm is applied over resized images from the UHD-LOL4k dataset, with a performance evaluation through the metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Image Quality Evaluator (NIQE), and Blind /Unreferenced Image Spatial Quality Evaluator (BRISQUE). According to the results, the proposed method outperforms algorithms with more complex architectures in the literature. The double convolutional architecture emphasizes local enhancement in real-world scenes and global enhancement in images with very low luminosity. Overall, this paper presents a significant contribution to low-light image enhancement offering an effective solution to the challenges posed by non-uniform luminance in real-world images.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ezequiel Perez-Zarate, Oscar Ramos-Soto, Erick Rodríguez-Esparza, and German Aguilar "LoLi-IEA: low-light image enhancement algorithm", Proc. SPIE 12675, Applications of Machine Learning 2023, 1267512 (4 October 2023); https://doi.org/10.1117/12.2677422

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available