TPCNet: A Low-Light Image Enhancement Network Inspired by Triple Physical Constraints

Low-light image enhancement is an essential computer visiontask to improve image contrast and to decrease the effects of colorbias and noise. Many existing interpretable deep-learning algorithmsexploit the Retinex theory as the basis of model design. However,previous Retinex-based algorithms, that consider reflected objects asideal Lambertian ignore specular reflection in the modeling process andconstruct the physical constraints in image space, limiting generalizationof the model. To address this issue, we preserve the specular reflectioncoefficient and reformulate the original physical constraints in theimaging process based on the Kubelka-Munk theory, thereby constructingconstraint relationship between illumination, reflection, and detection, theso-called triple physical constraints (TPCs) theory. Based on this theory,the physical constraints are constructed in the feature space of the modelto obtain the TPC network (TPCNet). Comprehensive quantitativeand qualitative benchmark and ablation experiments confirm that theseconstraints effectively improve the performance metrics and visual qualitywithout introducing new parameters, and demonstrate that our TPCNetoutperforms other state-of-the-art methods on 10 datasets. The code isavailable at https://github.com/2020shijingyi/TPCNet