Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning 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 realtime 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) Qlearning 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 sourcedomain 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 sourcedomain and targetdomain 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 realtime performance of maneuvering target recognition. Extensive experiments on PASCAL VOC datasets demonstrate the effectiveness of the proposed algorithm against the existing algorithms.

Light Field Reconstruction Based on Compressed Sensing via Deep Learning The light field has excellent application prospects in immersive media because of the abundant information of the light. Due to the sparsity and redundancy in light field images, light field reconstruction based on compressed sensing is used to recover light field images from only a few measurements. And the light field compressed sensing usually optimizes the measurement matrix and the dictionary and processes each of the light field images separately. Since the high similarity of light field images, the different viewpoints of images can be stacked together and formed as a 4D tensor. In this paper, we propose tensor based on compressed sensing (TCS) method to yield measurements with common characteristics. Besides, a better deep learning network is designed for TCS, the measurement matrix optimization and image reconstruction will be performed simultaneously. Experimental results show that the proposed method gets at least 3 dB gain in PSNR and outperforms stateoftheart in the reconstruction quality.