Multiple Point Spread Function Research Articles

New Sector Scan Geometry for High Frame Rate 2D-Echocardiography using Phased Arrays

2D echocardiography high frame rate techniques do have some drawbacks such as crosstalk artifacts caused by the interactions between the parallel transmitted and received beams. In this paper, we suggest a new cardiac imaging technique based on MLT (Multi-Line Transmission). The main idea of our approach is to benefit from the scan geometry to reduce the interference between the simultaneously transmitted beams. We propose to do the scan at different depths and in parallel to the diagonal scan sector. Therefore, compared to existing MLT techniques, the new scan sector strategy will result in artifacts' reduction in the ultrasound imaging systems. We entitled our approach the Synthetic Sum of Multi-Line Transmission (SS-MLT). Simulations of Point Spread Function (PSF), multiple Point Spread Functions (PSFs) and Cyst Phantom (CP) provided in this paper are compared in our approach to the main classical ultrasound imaging approaches. Therefore, the SS-MLT exhibits a very similar lateral profile as the Single Line Transmission (SLT) algorithm. Hence, the simulation results indicate a potential value of a future hardware implementation of SS-MLT technique.

Robust and Unified VLC Decoding System for Square Wave Quadrature Amplitude Modulation Using Deep Learning Approach

We have proposed a square wave quadrature amplitude modulation (SW-QAM) scheme for visible light communication (VLC) using an image sensor in our previous work. Here, we propose a robust and unified system by using a neural decoding method. This method offers essential SW-QAM decoding capabilities, such as LED localization, light interference elimination, and unknown parameter estimation, bundled into a single neural network model. This work makes use of a convolutional neural network (CNN) that has a capability in automatic learning of unknown parameters, especially when it deals with images as an input. The neural decoding method can provide good solutions for two difficult conditions that are not covered by our previous SW-QAM scheme: unfixed LED positions and multiple point spread functions (PSFs) of multiple LEDs. Responding to the above solutions, three recent CNN architectures- VGG, ResNet, and DenseNet-are modified to suit our scheme and other two small CNN architectures- VGG-like and MiniDenseNet-are proposed for low computing devices. Our experimental results show that the proposed neural decoding method performs better in terms of error rate than the theoretical decoding, an SW-QAM decoder with a Wiener filter, in different scenarios. Furthermore, we experiment on the problem of moving camera, i.e., the unfixed position of LED points. For this case, a spatial transformer network (STN) layer is added to the neural decoding method for solving the moving camera problem, and the method with the new layer achieves a remarkable result.

Psfgan: a generative adversarial network system for separating quasar point sources and host galaxy light

The study of unobscured active galactic nuclei (AGN) and quasars depends on the reliable decomposition of the light from the AGN point source and the extended host galaxy light. The problem is typically approached using parametric fitting routines using separate models for the host galaxy and the point spread function (PSF). We present a new approach using a Generative Adversarial Network (GAN) trained on galaxy images. We test the method using Sloan Digital Sky Survey (SDSS) r-band images with artificial AGN point sources added which are then removed using the GAN and with parametric methods using GALFIT. When the AGN point source PS is more than twice as bright as the host galaxy, we find that our method, PSFGAN, can recover PS and host galaxy magnitudes with smaller systematic error and a lower average scatter ($49\%$). PSFGAN is more tolerant to poor knowledge of the PSF than parametric methods. Our tests show that PSFGAN is robust against a broadening in the PSF width of $\pm 50\%$ if it is trained on multiple PSF's. We demonstrate that while a matched training set does improve performance, we can still subtract point sources using a PSFGAN trained on non-astronomical images. While initial training is computationally expensive, evaluating PSFGAN on data is more than $40$ times faster than GALFIT fitting two components. Finally, PSFGAN it is more robust and easy to use than parametric methods as it requires no input parameters.

Experimental assessment and analysis of super-resolution in fluorescence microscopy based on multiple-point spread function fitting of spectrally demultiplexed images

This paper presents an experimental assessment and analysis of super-resolution microscopy based on multiple-point spread function fitting of spectrally demultiplexed images using a designed DNA structure as a test target. For the purpose, a DNA structure was designed to have binding sites at a certain interval that is smaller than the diffraction limit. The structure was labeled with several types of quantum dots (QDs) to acquire their spatial information as spectrally encoded images. The obtained images are analyzed with a point spread function multifitting algorithm to determine the QD locations that indicate the binding site positions. The experimental results show that the labeled locations can be observed beyond the diffraction-limited resolution using three-colored fluorescence images that were obtained with a confocal fluorescence microscope. Numerical simulations show that labeling with eight types of QDs enables the positions aligned at 27.2-nm pitches on the DNA structure to be resolved with high accuracy.

Extended capture range for focus-diverse phase retrieval in segmented aperture systems using geometrical optics

Extending previous work by Thurman on wavefront sensing for segmented-aperture systems, we developed an algorithm for estimating segment tips and tilts from multiple point spread functions in different defocused planes. We also developed methods for overcoming two common modes for stagnation in nonlinear optimization-based phase retrieval algorithms for segmented systems. We showed that when used together, these methods largely solve the capture range problem in focus-diverse phase retrieval for segmented systems with large tips and tilts. Monte Carlo simulations produced a rate of success better than 98% for the combined approach.

MLEM Deconvolution of Protein X-Ray Diffraction Images Based on a Multiple-PSF Model

In this paper we analyze the degradation of protein X-ray diffraction images by diffuse light distortion (DLD). In order to correct the degradation, a new multiple point spread function (PSF) model is introduced and used to restore X-ray diffraction image data (XRD). Raw PSFs are collected from isolated spots in high-resolution areas on the diffraction patterns which represent the orientation of DLDs. An adaptive ridge regression (ARR) technique is used to remove noise from the raw PSF data. A target Gaussian function is used to model the raw PSFs. A maximum likelihood expectation maximization (MLEM) algorithm combined with a multi-PSF model is employed to restore high intensity, asymmetrical protein X-ray diffraction data. Experimental results using a single and multiple PSFs are presented and discussed. We show that using a multiple PSF model in the deconvolution algorithm improved the quality of the XRD and as a result the spot integration error (chi-squared) and corresponding electron density mapare improved.