LAE-Net: A locally-adaptive emb e dding network for low-light image enhancement

In the low-light enhancement task, one of the major challenges lies in how to balance the image en-hancement properties of light intensity, detail presentation and color fidelity. In natural scenes, the multi -distribution of frequency and illumination characteristics in the spatial domain makes the balance more difficult. To solve this problem, we propose a Locally-Adaptive Embedding Network, namely LAE-Net, to realize high-quality low-light image enhancement with locally-adaptive kernel selection and feature adaptation for multi-distribution issues. Specifically, for the frequency multi-distribution, we rethink the spatial-frequency characteristic of human eyes, experimentally explore the relationship among the re-ceptive field size, the image spatial frequency and the light enhancement properties, and propose an Entropy-Inspired Kernel-Selection Convolution, where each neuron can adaptively adjust the receptive field size according to its spatial frequency characterized by information entropy. For the illumination multi-distribution, we propose an Illumination Attentive Transfer subnet, where the neurons can simul-taneously sense global consistency and local details, and accordingly hint where to focus the efforts on, thereby adjusting the refined features. Extensive experiments with ablation analysis show the effective-ness of our method and the proposed method outperforms many related state-of-the-art techniques on four benchmark datasets: MEF, LIME, NPE and DICM.(c) 2022 Elsevier Ltd. All rights reserved.