Local Content-Aware Enhancement for Low-Light Images with Non-Uniform Illumination

In low-light image enhancement, prevailing Retinex-based methods often struggle with precise illumination estimation and brightness modulation. This can result in issues such as halo artifacts, blurred edges, and diminished details in bright regions, particularly under non-uniform illumination conditions. We propose an innovative approach that refines low-light images by leveraging an in-depth awareness of local content within the image. By introducing multi-scale effective guided filtering, our method surpasses the limitations of traditional isotropic filters, such as Gaussian filters, in handling non-uniform illumination. It dynamically adjusts regularization parameters in response to local image characteristics and significantly integrates edge perception across different scales. This balanced approach achieves a harmonious blend of smoothing and detail preservation, enabling more accurate illumination estimation. Additionally, we have designed an adaptive gamma correction function that dynamically adjusts the brightness value based on local pixel intensity, further balancing enhancement effects across different brightness levels in the image. Experimental results demonstrate the effectiveness of our proposed method for non-uniform illumination images across various scenarios. It exhibits superior quality and objective evaluation scores compared to existing methods. Our method effectively addresses potential issues that existing methods encounter when processing non-uniform illumination images, producing enhanced images with precise details and natural, vivid colors.