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In order to generate a denser focal stack, a cooperative generative adversarial network is proposed to learn the refocusing ability from light field imaging. To keep the axial continuity, the proposed framework is designed to learn features of a focal stack in both axial directions. Different from the classic generative adversarial network, our generative module consists of a forward prediction sub‐network and a backward prediction sub‐network, taking the forward‐and‐backward focal stacks as their inputs, respectively. The bi‐directional predictions are then fused by a weighting process, which is guided by an adversarial module. The proposed network is trained on light field focal stacks conducted via digital refocusing. Without loss of the refocus continuity, one can axial super‐resolve a focal stack by using the trained model. The effectiveness of the proposed algorithm on different types of focal stacks produced by both light fields and traditional camera shootings is validated. The experimental results indicate that the refocus variation of a focal stack can be well learned and predicted without a complete light field. Therefore, the proposed algorithm outperforms the traditional digital refocusing in terms of run‐time. [ABSTRACT FROM AUTHOR]

Axial light field resolution refers to the ability to distinguish features at different depths by refocusing. The axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. High refocusing precision can be essential for some light field applications like microscopy. In this paper, we propose a learning-based method to extrapolate novel views from axial volumes of sheared epipolar plane images (EPIs). As extended numerical aperture (NA) in classical imaging, the extrapolated light field gives re-focused images with a shallower depth of field (DOF), leading to more accurate refocusing results. Most importantly, the proposed approach does not need accurate depth estimation. Experimental results with both synthetic and real light fields show that the method not only works well for light fields with small baselines as those captured by plenoptic cameras (especially for the plenoptic 1.0 cameras), but also applies to light fields with larger baselines. [ABSTRACT FROM AUTHOR]

When using plenoptic camera for digital refocusing, angular undersampling can cause severe (angular) aliasing artifacts. Previous approaches have focused on avoiding aliasing by pre-processing the acquired light field via prefiltering, demosaicing, reparameterization, and so on. In this paper, we present a different solution that first detects and then removes angular aliasing at the light field refocusing stage. Different from previous frequency domain aliasing analysis, we carry out a spatial domain analysis to reveal whether the angular aliasing would occur and uncover where in the image it would occur. The spatial analysis also facilitates easy separation of the aliasing versus non-aliasing regions and angular aliasing removal. Experiments on both synthetic scene and real light field data sets (camera array and Lytro camera) demonstrate that our approach has a number of advantages over the classical prefiltering and depth-dependent light field rendering techniques. [ABSTRACT FROM PUBLISHER]

In light field imaging, axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. The refocusing precision can be essential for applications like light field microscopy. In this paper, we introduce a refocusing precision model based on a geometrical analysis of the flow of rays within the virtual camera. The model establishes the relationship between the feature separability of refocusing and different camera settings. As extending numerical aperture (NA) in classical imaging, the baseline extension of light field also gives more accurate refocusing results. To test the axial refocus precision, we conduct experiments with 1st generation Lytro camera as well as a Blender light field simulation. The results is basically consistent with our prediction. Then, we show that computationally extending the light field baseline increases the axial refocusing precision on real plenoptic camera and light field microscopy datasets. • Depth of field and refocusing precision are different notations. • Axial refocusing precision model for light field imaging. • Geometrical analysis of refocusing precision within the virtual camera. • Quantitative and qualitative experiments on axial refocusing precision boundaries. [ABSTRACT FROM AUTHOR]