Compared with traditional imaging, the light field contains more comprehensive image information and higher image quality. However, the available data for light field reconstruction are limited, and the repeated calculation of data seriously affects the accuracy and the real-time performance of multiperspective light field reconstruction. To solve the problems, this paper proposes a multiperspective light field reconstruction method based on transfer reinforcement learning. Firstly, the similarity measurement model is established. According to the similarity threshold of the source domain and the target domain, the reinforcement learning model or the feature transfer learning model is autonomously selected. Secondly, the reinforcement learning model is established. The model uses multiagent (i.e., multiperspective) Q-learning to learn the feature set that is most similar to the target domain and the source domain and feeds it back to the source domain. This model increases the capacity of the source-domain samples and improves the accuracy of light field reconstruction. Finally, the feature transfer learning model is established. The model uses PCA to obtain the maximum embedding space of source-domain and target-domain features and maps similar features to a new space for label data migration. This model solves the problems of multiperspective data redundancy and repeated calculations and improves the real-time performance of maneuvering target recognition. Extensive experiments on PASCAL VOC datasets demonstrate the effectiveness of the proposed algorithm against the existing algorithms.

中文翻译：

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通过转移强化学习的多视角光场重建方法。

与传统成像相比，光场包含更全面的图像信息和更高的图像质量。但是，用于光场重建的可用数据有限，并且数据的重复计算严重影响了多视角光场重建的准确性和实时性能。针对这一问题，本文提出了一种基于传递强化学习的多视角光场重建方法。首先，建立相似度度量模型。根据源域和目标域的相似度阈值，自主选择强化学习模型或特征转移学习模型。其次，建立了强化学习模型。该模型使用多主体（即，多角度）Q学习，以学习与目标域和源域最相似的功能集并将其反馈回源域。该模型增加了源域样本的容量，并提高了光场重建的准确性。最后，建立了特征转移学习模型。该模型使用PCA获得源域和目标域特征的最大嵌入空间，并将相似的特征映射到用于标签数据迁移的新空间。该模型解决了多角度数据冗余和重复计算的问题，提高了机动目标识别的实时性。在PASCAL VOC数据集上进行的大量实验证明了该算法相对于现有算法的有效性。