Tuning adaptive gamma correction (TAGC) for enhancing images in low ligh
This work addresses the challenge of low-light image enhancement for applications like night surveillance, medical imaging, and photography, but it appears incremental as it builds on existing gamma correction methods with adaptive tuning.
The paper tackled the problem of enhancing low-light images, which suffer from low contrast, noise, and blurred details, by proposing a tuning adaptive gamma correction (TAGC) model that automatically calculates gamma values based on color luminance, resulting in effective improvement in image quality while maintaining details and natural contrast.
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.