Abdulla, W.: Mask r-cnn for object detection and instance segmentation on keras and tensorflow.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., et al.: Tensorflow: Large-scale machine learning on heterogeneous distributed systems.Extensive experiments on multiple datasets demonstrate that our method can produce high fidelity enhancement results for low-light images and outperforms the current state-of-the-art methods by a large margin both quantitatively and visually. Moreover, a reinforcement-net further enhances color and contrast of the output image. With their guidance, the proposed multi-branch decomposition-and-fusion enhancement network works in an input adaptive way. The first attention map distinguishes underexposed regions from well lit regions, and the second attention map distinguishes noises from real textures. With the new dataset for training, our method learns two attention maps to guide the brightness enhancement and denoising tasks respectively. The dataset is much larger and more diverse than existing ones. To this end, we first construct a synthetic dataset with carefully designed low-light simulation strategies. To address this difficult problem, this paper proposes a novel end-to-end attention-guided method based on multi-branch convolutional neural network. Simply adjusting the brightness of a low-light image will inevitably amplify those artifacts. Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark.
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