A survey for light field super-resolution

Abstract

Compared to 2D imaging data, the 4D light field (LF) data retains richer scene’s structure information, which can significantly improve the computer’s perception capability, including depth estimation, semantic segmentation, and LF rendering. However, there is a contradiction between spatial and angular resolution during the LF image acquisition period. To overcome the above problem, researchers have gradually focused on the light field super-resolution (LFSR). In the traditional solutions, researchers achieved the LFSR based on various optimization frameworks, such as Bayesian and Gaussian models. Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities. In this paper, the present approach can mainly divided into conventional methods and deep learning-based methods. We discuss these two branches in light field spatial super-resolution (LFSSR), light field angular super-resolution (LFASR), and light field spatial and angular super-resolution (LFSASR), respectively. Subsequently, this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets. Finally, we discuss the potential innovations of the LFSR to propose the progress of our research field.

Keywords

Light field super-resolution / Convolutional neural network / Transformer / Sub-aperture image / Epipolar-plane image

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study is partially supported by the National Key R&D Program of China (2022YFC3803600), the National Natural Science Foundation of China (62372023), and the Open Fund of the State Key Laboratory of Software Development Environment, PR China (SKLSDE-2023ZX-11). Thank you for the support from HAWKEYE Group.

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