A hard-threshold based sparse inverse imaging algorithm for optical scanning holography reconstruction

The optical imaging takes advantage of coherent optics and has promoted the development of visualization of biological application. Based on the temporal coherence, optical coherence tomography can deliver three-dimensional optical images with superior resolutions, but the axial and lateral scanning is a time-consuming process. Optical scanning holography (OSH) is a spatial coherence technique which integrates three-dimensional object into a two-dimensional hologram through a two-dimensional optical scanning raster. The advantages of high lateral resolution and fast image acquisition offer it a great potential application in three-dimensional optical imaging, but the prerequisite is the accurate and practical reconstruction algorithm. Conventional method was first adopted to reconstruct sectional images and obtained fine results, but some drawbacks restricted its practicality. An optimization method based on l(2) norm obtained more accurate results than that of the conventional methods, but the intrinsic smooth of l(2) norm blurs the reconstruction results. In this paper, a hard-threshold based sparse inverse imaging algorithm is proposed to improve the sectional image reconstruction. The proposed method is characterized by hard-threshold based iterating with shrinkage threshold strategy, which only involves lightweight vector operations and matrix-vector multiplication. The performance of the proposed method has been validated by real experiment, which demonstrated great improvement on reconstruction accuracy at appropriate computational cost.