自动调节伽马值，让暗光图像更清晰自然。
Tuning adaptive gamma correction (TAGC) for enhancing images in low ligh
- 根据图像亮度自适应计算伽马系数，无需人工调参。
- 有效提升暗光图像对比度，保留细节与真实色彩。
- 适合夜视监控、医学成像等低光场景应用。
低光图像增强是计算机视觉中的重要挑战。光照不足导致图像对比度低、噪声大、细节模糊。本文提出一种名为自适应伽马校正调优（Tuning Adaptive Gamma Correction, TAGC）的模型。该模型通过分析图像色度亮度并计算平均颜色，自动且自适应地确定适合不同光照水平的伽马值，无需人为干预。定性与定量评估表明，TAGC能有效提升低光图像质量，保持细节、自然对比度和正确色彩分布，视觉效果更自然。该方法可广泛应用于夜间监控、医学图像增强及低光摄影等场景。
原文摘要 · Abstract (English)
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.
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