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Pattern-illuminated Fourier ptychography (piFP) is an elegant combination of structured illumination imaging and a Fourier ptychographic algorithm with the ability to image beyond the diffraction limit of the employed optics. Artifact-free piFP super-resolution reconstruction requires a high level of stability in the illumination pattern. However, unpredictable pattern variation occurs in the presence of environment perturbation, intensity fluctuation, and pointing instability at the source, leading to declines in image reconstruction quality. To address this issue, we present an efficient and robust piFP algorithm based on low-rank approximation (LRA-piFP), which relaxes the requirement for the stability of illumination patterns. This LRA-piFP method can model frame-wise pattern variation during a full scan, thus improve the reconstruction quality significantly. We take numerical simulations and proof-of-principle experiments with both long-range imaging and microscopy for demonstrations. Results show that the LRA-piFP method can handle different kinds of pattern variation and outperforms other state-of-the-art techniques in terms of reconstruction quality and resolution improvement. Our method provides effective experimental robustness to piFP with a natural algorithmic extension, paving the way for its application in both macroscopic and microscopic imaging. [ABSTRACT FROM AUTHOR]

Synthetic aperture imaging systems can be applied to equivalently get high-resolution images of traditional monolithic primary mirror systems with less weight and costs. Spatial modulation diversity (SMD), a newly developed post-processing technology applicable for such synthetic aperture systems, is very sensitive to Gaussian noise, which greatly limits its further application. In this paper, we propose an improved SMD strategy by introducing the deep denoising convolutional neural networks (DnCNN) into the image preprocessing to improve the robustness of SMD. Results of the numerical simulations demonstrate that the strategy proposed exhibits superior performance compared to the traditional SMD technique in terms of both the root-mean-square error (RMSE) of phase estimates and the structural similarity (SSIM) of image reconstruction. In view of the superiority and robustness, the method we proposed may have important application prospects in multi-aperture imaging systems. [ABSTRACT FROM AUTHOR]

We propose and demonstrate a method of spatially overlapping diversity technology for wavefront sensing and image restoration with undersampled systems, which can handle extended objects illuminated by spatially incoherent light. The technique first scans a smaller subaperture across the large pupil of undersampled systems to generate several properly sampled images. Then, a nonlinear optimization method is used for joint reconstruction of wavefront and object. Simulations performed with different phase distributions initially validate the technique. Furthermore, an experimental configuration is established to measure the loaded defocus and wavefront transmitted through a plano-convex lens. A detailed analysis of experimental results is presented to demonstrate the effectiveness of this technique. [ABSTRACT FROM PUBLISHER]

• In local denoising, the nonlocal WDF filter provides better results. • The WDF filter can be realized with a hardware implementation, such as an optical system. • Simple filters, such as apertures and Gaussian-weighted averages, can be highly effective in reducing noise. Nonlocal means denoising (NLM) and block-matching and 3D filtering (BM3D) have become the most popular algorithms for image processing. The disadvantage of these methods is that they cannot be realized by hardware implementation. The Wigner distribution function (WDF) has advantages such as explicit physical meaning, spatial frequency distribution of each pixel, and suitability for optical implementation. In recent years, the wide-field imaging system has been used extensively in multiple situations such as aviation, medicine, and traffic. Large-field images often have multiple point spread functions (PSF) and local random noise. However, the results have shown that most nonlocal filters have inadequate effects on local noise filtering. This paper analytically examines the Wigner transform of a multi-Gaussian function and investigates the effects of cross-term and noise on filter reconstruction in image processing. Based on the results, a new space-variant filter with a simple mathematical formula is proposed and applied to local high-frequency noise filtering. Since the filter is closely related to the intensity distribution in the spatial and frequency domains, it provides a simple way to process local noise while simultaneously preserving the high-frequency information of the image. [ABSTRACT FROM AUTHOR]

• We propose a simple but efficient pre-processing algorithm capable of extracting translation positions of the structure light directly from raw images of Incoherent pattern-illumination Fourier ptychography (IFP), termed translation position extracting (TPE). • TPE removes the requirement of IFP for prior knowledge of translation positions, thus further relaxing the practical conditions. Both simulation and experiment are performed to demonstrate its effectiveness. • The calculation cost of the TPE algorithm is similar to the cross-correlation, which is far lower than the existing translation extraction algorithm based on iterative optimization. Incoherent Pattern-illuminated Fourier ptychography (IFP) is a newly developed super-resolution method, where accurate prior knowledge of translation positions is essential for image reconstruction. To release this limitation, we propose a pre-processing algorithm capable of extracting translation positions of the structure light directly from raw images of IFP, termed translation position extracting (TPE). TPE mainly involves two steps. First, the speckle parts mixed in the acquired raw images, in which the illumination motion is encoded, are isolated by intensity averaging and division. Then the cross-correlations of the speckle dataset are computed to determine the shift positions. TPE-IFP improves the previous IFP by removal of the requirement for prior knowledge of translation positions. Its effectiveness is demonstrated by obtaining high-quality super-resolution images in absence of location information in both simulations and experiments. By further relaxing the practical conditions, the proposed TPE may accelerate the applications of IFP. What's more, as a pre-processing approach, TPE might also contribute to the estimation of pattern positions for the similar speckle-based imaging. [ABSTRACT FROM AUTHOR]