Learning based Deep Disentangling Light Field Reconstruction and
  Disparity Estimation Application
Langqing Shi Ping Zhou
Light field cameras have a wide range of uses due to their ability to
simultaneously record light intensity and direction. The angular resolution of
light fields is important for downstream tasks such as depth estimation, yet is
often difficult to improve due to hardware limitations. Conventional methods
tend to perform poorly against the challenge of large disparity in sparse light
fields, while general CNNs have difficulty extracting spatial and angular
features coupled together in 4D light fields. The light field disentangling
mechanism transforms the 4D light field into 2D image format, which is more
favorable for CNN for feature extraction. In this paper, we propose a Deep
Disentangling Mechanism, which inherits the principle of the light field
disentangling mechanism and further develops the design of the feature
extractor and adds advanced network structure. We design a light-field
reconstruction network (i.e., DDASR) on the basis of the Deep Disentangling
Mechanism, and achieve SOTA performance in the experiments. In addition, we
design a Block Traversal Angular Super-Resolution Strategy for the practical
application of depth estimation enhancement where the input views is often
higher than 2x2 in the experiments resulting in a high memory usage, which can
reduce the memory usage while having a better reconstruction performance.