DBCE-Net: discrete brightness curve estimation for low-light image enhancement

The curve-based lighting adjustment technique is widely used in fields such as photography and image processing. The deep learning-based lighting curve adjustment method has shown excellent performance in the field of low-light image enhancement. However, existing curve-based deep learning methods tend to use complex mathematical formulas to define the curve model and add a large number of regularization constraints to ensure that the curve conforms to real physical scenes. This limits the flexibility of the lighting curve, making it unable to accurately enhance brightness for low-light images, resulting in problems such as regional color distortion and overall color bias. To solve this problem, we propose a novel low-light image enhancement model called Discrete Brightness Curve Estimation (DBCE-Net). In DBCE-Net, we introduce a new method for defining curves to enhance regional illumination more effectively. At the same time, we propose a discrete parameter calculation network based on mutual attention mechanism to estimate the discrete brightness adjustment curve from low-light images. Finally, we use a multi-scale denoising network to handle noise introduced by brightness enhancement in shadow areas. Extensive experiments on various datasets have demonstrated that our DBCENet achieves competitive performance in terms of objective quantitative metrics and subjective visual quality evaluation.