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Deep Reinforcement Learning based Patch Selection for Illuminant Estimation

Xu, Bolei; Liu, Jingxin; Hou, Xianxu; Liu, Bozhi; Qiu, Guoping

Authors

Bolei Xu

Jingxin Liu

Xianxu Hou

Bozhi Liu

GUOPING QIU GUOPING.QIU@NOTTINGHAM.AC.UK
Professor of Visual Informationprocessing



Abstract

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 selection of noisy patches that would affect estimation accuracy. In this paper, we regard patch selection in neural network based illuminant estimation as a controlling problem of selecting image patches that could help remove noisy patches and improve estimation accuracy. To achieve this, we construct a selection network (SeNet) to learn a patch selection policy. Based on data statistics and the learning progression state of the deep illuminant estimation network (DeNet), the SeNet decides which training patches should be input to the DeNet, which in turn gives feedback to the SeNet for it to update its selection policy. To achieve such interactive and intelligent learning, we utilize a reinforcement learning approach termed policy gradient to optimize the SeNet. We show that the proposed learning strategy can enhance the illuminant estimation accuracy, speed up the convergence and improve the stability of the training process of DeNet. We evaluate our method on two public datasets and demonstrate our method outperforms state-of-the-art approaches.

Citation

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, https://doi.org/10.1016/j.imavis.2019.08.002

Journal Article Type Article
Acceptance Date Aug 3, 2019
Online Publication Date Aug 26, 2019
Publication Date Nov 1, 2019
Deposit Date Oct 8, 2019
Publicly Available Date Aug 27, 2020
Journal Image and Vision Computing
Print ISSN 0262-8856
Electronic ISSN 1872-8138
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 91
Article Number 103798
DOI https://doi.org/10.1016/j.imavis.2019.08.002
Keywords Color constancy; Reinforcement learning; Patch selection
Public URL https://nottingham-repository.worktribe.com/output/2782804
Publisher URL https://www.sciencedirect.com/science/article/pii/S0262885619301179?via%3Dihub

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