In this study, a low-light image enhancement algorithm based on multiscale depth curve estimation is proposed to address the poor generalization ability of existing algorithms. Low-light image enhancement is achieved by learning the mapping relationship between normal images and low-light images with different scales. The parameter estimation network comprises three encoders with different scales and a fusion module, facilitating the efficient and direct learning for low-light images. Furthermore, each encoder comprises cascaded convolutional and pooling layers, thereby facilitating the reuse of feature layers and improving computational efficiency. To enhance the constraint on image brightness, a bright channel loss function is proposed. The proposed method is validated against six state-of-the-art algorithms on the LIME, LOL, and DICM datasets. Experimental results show that enhanced images with vibrant colors, moderate brightness, and significant details can be obtained using the proposed method, outperforming other conventional algorithms in subjective visual effects and objective quantitative evaluations.