Non-uniform-lighting Image Enhancement Based on Centripetal-autowave Intersecting Cortical Model
基于向心自动波交叉皮质模型的非均匀光照图像增强

For non-uniform-lighting images, we propose an image enhancement technique based on centripetal-autowave intersecting cortical model (CA-ICM). The original ICM possesses the autowave nature stemming from the connection function during the firing process, but poses a problem called interference, which could blur the edge and detail in image processing tasks. To solve it, the implementation of CA based on morphological median set is presented. As for the relationship between the input and output of CA-ICM, we apply an adaptive S shape non-linear mapping function based on image characteristics of key value. Furthermore, we label and restore those unfired positions for algorithm's robustness. A modified non-linear color restoration process based on chromatic information is applied finally. The precise mapping function and optimized parameters considered in detail lead to better experimental results, indicating that the CA-ICM shows more advantages than ICM. Efficient dynamic range adjustment is conducted, especially the highlight inhibition and shadow rendition with details. The CA creates the lateral inhibition effect, leading to nature, sharp, and colorful outputs with high objective evaluations.