Abstract
The sampling patterns of the light field microscope (LFM) are highly depth-dependent, which implies non-uniform recoverable lateral resolution across depth. Moreover, reconstructions using state-of-the-art approaches suffer from strong artifacts at axial ranges, where the LFM samples the light field at a coarse rate. In this work, we analyze the sampling patterns of the LFM, and introduce a flexible light field point spread function model (LFPSF) to cope with arbitrary LFM designs. We then propose a novel aliasing-aware deconvolution scheme to address the sampling artifacts. We demonstrate the high potential of the proposed method on real experimental data.
Highlights
Since it was first proposed by Levoy et al in 2006 [1], light field microscopy has proven very useful in biological applications involving fast dynamics, due to its high speed 3D imaging capability
In this work we study the depth-dependent sampling patterns of a light field microscope and analyze how they introduce aliasing, in order to understand the cause of the artifacts; to the extent of our knowledge, such an analysis has not been performed for the LFM before
This refers to the sampling rate we chose for rendering the volumes and has nothing to do with the actual details that can be recovered, which is the effective resolution of the LFM
Summary
Since it was first proposed by Levoy et al in 2006 [1], light field microscopy has proven very useful in biological applications involving fast dynamics, due to its high speed 3D imaging capability. The light field microscope (LFM) enables scan-less 3D imaging of fluorescent specimens by incorporating an array of micro-lenses into the optical path of a conventional wide-field microscope. Both spatial and directional light field information is captured in a single shot, allowing for subsequent volumetric reconstruction of the imaged sample. The methods for rendering extended depth of field images from plenoptic devices were limited to lenslet resolution [8], which is the number of available micro-lenses. On the other hand, inspired by the large amount of work on computational super-resolution in the computer vision field [14,15], algorithms for super-resolving the light field were developed, involving multi-view reconstruction [16,17], or explicit image formation models for plenoptic devices employing either ray-based [18,19,20] or wave-based optics [13,21,22]
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