# GS-AGC: An Adaptive Glare Suppression Algorithm Based on Regional Brightness Perception
## 2 Related Work  
Low-light image enhancement: Low-light images are primarily caused by insufficient lighting in the shooting environment. They are characterized by a limited grayscale range, high spatial correlation among neighboring pixels, and minimal variation in grayscale, resulting in narrow information representation of objects, backgrounds, details, and noise. Two types of low-light images exist: dark-light images and wide-dynamic-range images. Dark-light images typically exhibit large regions of darkness, with gray values concentrated within a low range, leading to minimal image contrast. On the other hand, wide-dynamic-range images have an uneven distribution of light, with both extremely dark and bright regions present. For this reason, one of the main characteristics of a low-light image is dark light regions occupying the main part of the image. Some techniques commonly used in recent years include Zero-DCE [20], gamma correction [21], dark channel a priori [22], and histogram equalization [23]. Histogram equalization enhances an image by adjusting the grayscale of each pixel. Gamma correction improves the grayscale value of darker regions through a nonlinear transformation. Dark channel a priori leverages noise distribution characteristics, specifically foggy characteristics of glare noise, to enhance image color and visibility. Zero-DCE, or Zero Reference Depth Curve Estimation, utilizes a lightweight network (DCE-NET) to predict higher-order curves at the pixel level, which are then employed as adjustments to expand the input's range of variation. All of the above methods show excellent performance in low-light image enhancement but cannot satisfy the real-time nature of low-light image processing. Gamma correction algorithm: The response curve of early CRT monitors is the origin of gamma [24]. This refers to the nonlinear relationship between the output brightness and input voltage. Through Gamma-encoded data and Gamma characteristics of CRT monitors, the product of these two sets of encoding results in exactly 1 [25]. The Gamma curve is a special kind of hue curve, and Gamma correction [26; 27] is the editing of this curve for image processing. It belongs to one of the grayscale transformation methods. The Gamma value is a crucial parameter in this process, as it determines the degree of change in brightness and contrast. Larger Gamma values correspond to smaller changes in brightness and contrast, resulting in darker images. Conversely, smaller Gamma values result in greater changes in brightness and contrast, leading to brighter images. The mathematical expression for the Gamma correction formula is:  
\[Vout=Vin^{\gamma} \tag{1}\]  
where _Vout_ denotes the output luminance value, _Vin_ denotes the input luminance value, and \(\gamma\) denotes the Gamma value. When the Gamma value is equal to 1, the output luminance value is equal to the input luminance value and the image does not change; when the Gamma value is greater than 1, the output luminance value is smaller than the input luminance value, and the image becomes darker; when the Gamma value is less than 1, the output luminance value is greater than the input luminance value, and the image becomes brighter. While Gamma correction aims to correct the image from a linear response to a response that aligns better with human eye perception through a nonlinear transformation, it should be noted that this global processing method does not have the ability to selectively adjust specific regions of interest in the image. There is a possibility of overlooking bright regions and solely focusing on dark light regions during the image processing stage. Luminance perception by the human eye: For most sensory and perceptual stimuli, their performance follows the Weber-Fechner law [28]. The Weber-Fechner Law [29] is a law in psychophysics that aims to explain the relationship between the intensity of a stimulus and the corresponding perceived response in humans.