Automatic Photo Enhancer Using Machine Learning and Deep Learning with Python

Author:

Saravanan S.,Shingloo Hemal,Sajid Nameera,Lamba Navneet,Pritam Akshyansu,Srivastava Anupam

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Ma, Y.: Low-light image enhancement method based on deep learning. Inf. Technol. 45(1), 5 (2021)

2. Chen, Q., Xu, J., Koltun, V.: Fast image processing with fully-convolutional networks. In: IEEE International Conference on Computer Vision (ICCV) 2017, pp. 2516–2525 (2017)

3. Yang, W., Wang, S., Fang, Y., Wang, Y., Liu, J.: From fidelity to perceptual quality: a semi-supervised approach for lowlight image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3063–3072 (2020)

4. Hu, Y., He, H., Xu, C., Wang, B., Lin, S.: Exposure: a white-box photo post-processing framework. ACM Trans Graph (TOG). 37(2), 1–17 (2018)

5. Yu, R., Liu, W., Zhang, Y., Qu, Z., Zhao, D., Zhang, B.: Deep exposure: learning to expose photos with asynchronously reinforced adversarial learning. In: Advances in Neural Information Processing Systems 31, pp. 2149–2159. Curran Associates (2018)

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