Curve Enhancement: A No-Reference Method for Low-Light Image Enhancement

In this paper, we introduce an end-to-end method for enhancing low-light images without relying on paired datasets. Our solution is reference-free and unsupervised, addressing the lack of real-world low-light paired datasets effectively. Specifically, we design a Brightness Boost Curve (BB-Curve) that enhances the brightness of image pixels in a finely mapped form. Additionally, we propose a lightweight deep neural network that can estimate the curve parameters and evaluate the quality of the enhanced images using a series of no-reference loss functions. We validate our method through experiments conducted on several datasets and provide both subjective and quantitative evaluations to demonstrate its significant brightness enhancement capabilities, free from smearing and artifacts. Notably, our approach displays a strong ability to generalize while retaining details that are crucial for image interpretation. With the reduced network structure and simple curve mapping, our model yields superior training speed and the best prediction performance among comparative methods.