Sub-Diffraction Visible Imaging Using Macroscopic Fourier Ptychography and Regularization by Denoising.

Zhixin Li,Xin Wei,Desheng Wen,Gang Liu,Weikang Zhang,Zongxi Song

doi:10.3390/s18093154

Abstract

Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications.

Highlights

Improving the resolution of an imaging system is a long-term goal in imaging sciences
The simulated captured images are corrupted with Gaussian noise and laser speckle noise, respectively
A new Fourier ptychography (FP) reconstruction method is proposed for sub-diffraction imaging that can

Summary

Introduction

Improving the resolution of an imaging system is a long-term goal in imaging sciences. It has great importance in many optical implementations and computer vision, including medical imaging, remote sensing, and surveillance. In long range imaging, the limited angular extent of the aperture results in low spatial resolution. Several methods have been proposed to prevent resolution loss, and the most direct way is to increase the input aperture by using a large lens. It is not an ideal solution to physically increase the lens diameter, which leads to expensive and heavy setups. Super-resolution reconstruction is a kind of computational imaging technique, which can improve the spatial resolution by capturing and processing a collection of low-resolution (LR)

Methods

Results

Conclusion