Tuning adaptive gamma correction (TAGC) for enhancing images in low ligh

TL;DR

This paper introduces Tuning Adaptive Gamma Correction (TAGC), an automatic method for enhancing low-light images by analyzing luminance and adjusting gamma values to improve contrast and detail without manual intervention.

Contribution

The paper proposes a novel adaptive gamma correction model that automatically adjusts gamma based on image luminance, improving low-light image quality without manual tuning.

Findings

Effectively enhances low-light images with improved contrast and detail.

Maintains natural color and visual quality in processed images.

Applicable to various low-light imaging scenarios like surveillance and medical imaging.

Abstract

Enhancing images in low-light conditions is an important challenge in computer vision. Insufficient illumination negatively affects the quality of images, resulting in low contrast, intensive noise, and blurred details. This paper presents a model for enhancing low-light images called tuning adaptive gamma correction (TAGC). The model is based on analyzing the color luminance of the low-light image and calculating the average color to determine the adaptive gamma coefficient. The gamma value is calculated automatically and adaptively at different illumination levels suitable for the image without human intervention or manual adjustment. Based on qualitative and quantitative evaluation, tuning adaptive gamma correction model has effectively improved low-light images while maintaining details, natural contrast, and correct color distribution. It also provides natural visual quality. It can be considered a more efficient solution for processing low-light images in multiple applications such as night surveillance, improving the quality of medical images, and photography in low-light environments.