Control-Lit: Illumination Controllable Backlit Image Enhancement

Backlit image enhancement (BIE) aims to address image degradation caused by challenging lighting conditions. By enhancing the illumination of underexposed areas and restoring image details while avoiding overexposure, it achieves an overall harmonious luminance. In contrast to traditional BIE methods that apply global enhancements with limited effectiveness, we propose a controllable Mamba-based enhancement method, termed Control-Lit. Our method not only delivers effective backlit image enhancement but also allows users to interactively adjust the illumination of specific regions. Control-Lit achieves superior global enhancement performance by employing the Dark Channel Prior (DCP)-based Finite Scalar Quantization (DFSQ) module that provides a high-quality image prior. Additionally, the Dark Channel Prior Enhancement (DCPE) module is designed to guide the network in pixel-wise illumination adjustment at the feature level, thereby achieving enhancement of backlit regions. Furthermore, to address the loss of content and details in backlit regions, the Global-Local Vision State Space (GLVSS) module is incorporated to extract both global and local features for BIE. To enable customizable, controllable enhancement, we introduce an illumination control vector. By adjusting the coefficient map elements that are multiplied by this vector, users can achieve precise regional illumination adjustments. To further validate the generalization of different light enhancement methods, we contribute a synthetic backlit image dataset using a relighting generative model. Along with current widely used datasets, the experimental results demonstrate that our method achieves state-of-the-art performance on all datasets while enabling high-quality and controllable image illumination adjustment. The code is available at https://github.com/wuhj43/Control-Lit.