Adaptive complex illumination image enhancement algorithm based on Retinex

With the expansion of head-mounted displays into various professional domains, there is an increasing demand for adaptive complex illumination image enhancement algorithms that are computationally efficient and resource-conservative. This study introduces a method to categorize input images into high-light, normal, and low-light images based on brightness thresholds. Enhanced Retinex-based algorithms are proposed to process these categories. For low-light images, operations such as histogram equalization and sharpening are applied to enhance the details of the illumination component. For highlight images, an illumination component estimation method is utilized to effectively reduce noise and enhance contour information, followed by normalization using a sigmoid function. The effectiveness of the low-light enhancement algorithm is validated using the LOL dataset. The high-light enhancement algorithm is validated using a self-constructed dataset.