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Deep Reinforcement Learning based Patch Selection for Illuminant Estimation (2019)
Journal Article
Xu, B., Liu, J., Hou, X., Liu, B., & Qiu, G. (2019). Deep Reinforcement Learning based Patch Selection for Illuminant Estimation. Image and Vision Computing, 91, Article 103798. https://doi.org/10.1016/j.imavis.2019.08.002

Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model. However, such practices would inevitably lead to information loss or the... Read More about Deep Reinforcement Learning based Patch Selection for Illuminant Estimation.

Improving variational autoencoder with deep feature consistent and generative adversarial training (2019)
Journal Article
Hou, X., Sun, K., Shen, L., & Qiu, G. (2019). Improving variational autoencoder with deep feature consistent and generative adversarial training. Neurocomputing, 341, 183-194. https://doi.org/10.1016/j.neucom.2019.03.013

We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we... Read More about Improving variational autoencoder with deep feature consistent and generative adversarial training.

Visual quality assessment for super-resolved images: database and method (2019)
Journal Article
Zhou, F., Yao, R., Liu, B., & Qiu, G. (2019). Visual quality assessment for super-resolved images: database and method. IEEE Transactions on Image Processing, 28(7), 3528-3541. https://doi.org/10.1109/tip.2019.2898638

Image super-resolution (SR) has been an active re-search problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR perfor-mance ha... Read More about Visual quality assessment for super-resolved images: database and method.