An Algorithm for Automatic Exposure Control

Automatic exposure is a common image processing technique used to adjust image brightness and maintain appropriate exposure levels under various lighting conditions. This algorithm analyzes the brightness distribution of an image and automatically adjusts exposure parameters to ensure the image is neither too dark nor too bright.

The core logic of this algorithm is based on adjusting the average brightness value of the image. First, the algorithm calculates the overall mean brightness of the current image, typically implemented using a function like cv2.mean() or numpy.mean() for efficient computation. It then compares this value with a predefined target brightness level. If the current brightness is too high or too low, the algorithm automatically adjusts exposure parameters (such as gain or exposure time) through control loops to bring the image brightness closer to the ideal value. The main advantage of this approach lies in its computational simplicity and rapid response, making it suitable for real-time image processing applications.

Furthermore, the algorithm can be optimized by incorporating image histogram analysis. For example, histogram equalization techniques can be applied to prevent extreme variations in overexposed or underexposed regions, thereby improving the final visual quality. By implementing appropriate target brightness selection and dynamic range adjustment mechanisms through histogram stretching or tone mapping, this algorithm demonstrates excellent adaptability across different lighting conditions.