| Summary: | Light-field cameras can simultaneously collect the position and angle details of light in space through a single exposure, which possesses unique advantages in the field of depth estimation.As the depth labels of light-field real-scene datasets are difficult to obtain and the accuracy is not high, most existing light-field depth estimation methods rely on a large number of light-field synthetic scene datasets for training.However, the difference in the image feature distributions between the synthetic and real datasets leads to deviations in the mapping relationship between the sub-aperture image and depth map learned by the network in the synthetic dataset when applied to the real dataset.A new light-field depth estimation method is proposed in this study.First, the image translation network based on adversarial learning is used to approximate the feature distribution of the real-scene image using the synthetic-scene-centered sub-aperture image, thereby implementing the multi-view angle consistency constraint in the image translation network to ensure the sub-apertures of different views before and after image translation.The disparity relationship between the images does not change.Second, a multi-channel Dense Connection(DC) depth estimation network is designed, in which the multi-channel input module extracts the features of sub-aperture image stacks along different directions.The feature fusion is performed using the DC module, which improves the efficiencies of network feature extraction and feature transfer.Finally, the experimental results of the light-field synthetic dataset, i.e., 4D Light Field Benchmark, and light-field real dataset, i.e., Stanford Lytro Light Field, indicate that the values of the Mean Square Error(MSE) and Bad Pixel(BP) indicators of the proposed network are reduced by 23.3% and 8.6% compared with the Baseline network results, which are comparable to the existing ones.Compared with the EPINET, EPI_ORM, and EPN+OS+GC methods, the proposed estimation method based on the network above effectively improves depth estimation accuracy and demonstrates better robustness and generalization ability.
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