Abstract
Three modifications are shown to improve resolution and reduce noise amplification in endoscopic imaging through multi-mode fiber using optimization-based reconstruction (OBR). First, random sampling patterns are replaced by sampling patterns designed to have more nearly equal singular values. Second, the OBR algorithm uses a point-spread function based on the estimated spatial frequency spectrum of the object. Third, the OBR algorithm gives less weight to modes having smaller singular values. In simulations for a step-index fiber supporting 522 spatial modes, the modifications yield a 20% reduction in image error (l(2) norm) in the noiseless case, and a further 33% reduction in image error at a 22-dB shot noise-limited SNR as compared to the original method using random sampling patterns and OBR.
Highlights
The use of multi-mode fibers (MMFs) for imaging has long been of interest [1,2], and the past few years have seen productive developments in the field of MMF endoscopic imaging [3,4,5,6,7,8,9,10,11,12]
The optimization-based image reconstruction (OBR) algorithm decomposes the set of random intensity sampling patterns into a set of intensity modes using a singular-value decomposition (SVD), and reconstructs the image as a linear combination of the intensity modes based on the powers reflected from the object when sampled by the random intensity patterns
The advantages of OBR using random sampling patterns are faster calibration time and higher resolution compared to localized reconstruction, which come at the expense of noise amplification
Summary
The use of multi-mode fibers (MMFs) for imaging has long been of interest [1,2], and the past few years have seen productive developments in the field of MMF endoscopic imaging [3,4,5,6,7,8,9,10,11,12]. One endoscopic MMF imaging method [3] uses random intensity patterns to sample an object and employs an optimization-based image reconstruction (OBR) algorithm. This method at once enhances the resolution to four times the number of spatial modes propagating in the MMF and requires the least complicated calibration setup among all imaging methods. The OBR algorithm decomposes the set of random intensity sampling patterns into a set of intensity modes using a singular-value decomposition (SVD), and reconstructs the image as a linear combination of the intensity modes based on the powers reflected from the object when sampled by the random intensity patterns.
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