LAE-Net: A locally-adaptive embedding network for low-light image enhancement

•LAE-Net goes beyond the limitations of previous works and explores the relationship between image characteristics and image quality from the data itself, so as to guide the high-quality image generation.•The entropy-inspired kernel-selection convolution can adaptively adjust the receptive field size according to its spatial frequency characterized by information entropy.•The illumination attention transfer sub-net can simultaneously sense global consistency and local details, thereby adjusting the refined features.•LAE-Net can balance different local enhancement requirements of properties of light intensity, detail presentation and color fidelity, and produce high-quality and visual-pleasing normal light images.