Abstract: To address the issues of overexposure, color distortion and detail loss in existing enhancement methods when image illumination distribution is uneven, a low-light image enhancement method combining dark region guidance and attention mechanism is proposed. Firstly, the simple linear iterative clustering (SLIC) method is used to generate a dark region guidance map, which guides the network to enhance the underexposed regions of the image while ensuring that the normally exposed regions are not overexposed. Secondly, a channel attention module is designed to improve the extraction of color information, effectively restoring the image color while maintaining natural color fidelity. Subsequently, a global context module is established to enhance the network’s global perception capability, enriching image details. Finally, an enhancement network is designed to fuse the input features with the output features of the dark area attention network,achieving contrast re-enhancement. Multiple comparative experiments are conducted on six public datasets to compare the performance from both subjective and objective aspects. It is shown that the proposed method effectively solves the problems of color distortion, detail loss and uneven exposure in low-light images, delivering superior visual enhancement effect and generalizability.

Key words: low-light image enhancement, dark region guidance, channel attention module, global context module, deep learning