This paper proposes an adaptive image brightness processing method combining an improved two-dimensional gamma function and secondary correction to address challenges in complex lighting conditions, such as overexposure, low illumination, and uneven lighting. Existing methods, including adaptive histogram equalization, Retinex-based approaches, and deep learning models, exhibit limitations like noise amplification, color distortion, or high computational demands. To overcome these issues, the proposed method first converts RGB images to HSV space for illumination component extraction using multi-scale Gaussian functions. A target brightness-based two-dimensional gamma function is then applied for primary correction, dynamically adjusting pixel brightness to enhance dark regions and suppress overexposed areas. A secondary correction step further suppresses highlights using a linear adjustment function with quadratic and exponential coefficients, preserving image details. Comparative experiments against CLAHE, MSRCR, and existing gamma-based methods demonstrate the superiority of the proposed approach in terms of standard deviation, information entropy, and average gradient metrics. Results show significant improvements in contrast, detail preservation, and highlight suppression across low, medium, and high lighting environments. The method effectively balances subjective quality and objective performance, offering robust adaptability for practical applications in machine vision and image enhancement tasks.