Super-resolution imaging through scattering media based on improved triple correlation recursion and deterministic iterative estimation.

Iterative phase retrieval algorithms are commonly used in computational techniques and optimization methods to obtain the reconstruction of objects hidden behind opaque scattering media. However, these methods are susceptible to converging to incorrect local minima, and the calculation results tend to be unstable. In this paper, a triple-correlation-based super-resolution imaging (TCSI) framework is proposed to achieve single-shot imaging of unknown objects hidden behind the scattering medium. The amplitude spectrum of the object is obtained by a speckle correlation (SC) method. Iterative relaxation recursion (IRR) sufficiently extracts object information from the triple correlation (TC) of the speckle patterns, serving as the prior initial guess for the iterative estimation algorithm (IE) to obtain a deterministic phase spectrum. Blur correction (BC) is then applied to the diffraction-limited image to achieve super-resolution imaging. Experimental results demonstrate that the flexible framework could effectively overcome the influence of speckle resolution and outperform traditional methods in terms of performance. Our approach provides a basis for non-invasively visualizing various samples behind scattering media.

Self-Modulated Ghost Imaging in Dynamic Scattering Media.

In this paper, self-modulated ghost imaging (SMGI) in a surrounded scattering medium is proposed. Different from traditional ghost imaging, SMGI can take advantage of the dynamic scattering medium that originally affects the imaging quality and generate pseudo-thermal light through the dynamic scattering of free particles' Brownian motion in the scattering environment for imaging. Theoretical analysis and simulation were used to establish the relationship between imaging quality and particle concentration. An experimental setup was also built to verify the feasibility of the SMGI. Compared with the reconstructed image quality and evaluation indexes of traditional ghost imaging, SMGI has better image quality, which demonstrates a promising future in dynamic high-scattering media such as dense fog and turbid water.

Image dehazing algorithm based on optimized dark channel and haze-line priors of adaptive sky segmentation.

When dealing with outdoor hazy images, traditional image dehazing algorithms are often affected by the sky regions, resulting in appearing color distortions and detail loss in the restored image. Therefore, we proposed an optimized dark channel and haze-line priors method based on adaptive sky segmentation to improve the quality of dehazed images including sky areas. The proposed algorithm segmented the sky region of a hazy image by using the Gaussian fitting curve and prior information of sky color rules to calculate the adaptive threshold. Then, an optimized dark channel prior method was used to obtain the light distribution image of the sky region, and the haze-line prior method was utilized to calculate the transmission of the foreground region. Finally, a minimization function was designed to optimize the transmission, and the dehazed images were restored with the atmospheric scattering model. Experimental results demonstrated that the presented dehazing framework could preserve more details of the sky area as well as restore the color constancy of the image with better visual effects. Compared with other algorithms, the results of the proposed algorithm could achieve higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation values and provide the restored image with subjective visual effects closer to the real scene.

Dual-camera compressive hyperspectral imaging based on deep image prior and a guided filter.

Coded aperture snapshot spectral imaging (CASSI) aims to capture the high-dimensional (usually 3D) data cube using a 2D sensor in a single snapshot. Due to the ill-posed snapshot, the reconstruction results are not ideal. One feasible solution is to utilize additional information such as the panchromatic measurement in CASSI. In this paper, we propose a dual-camera hyperspectral reconstruction method based on the deep image prior (DIP) and a guided filter. In particular, the panchromatic measurements are used to estimate spatial detail, and spectral details are provided using CASSI measurements. These measurements are used as a priori learning by the self-supervised network. Using iteration combined with DIP, the hyperspectral reconstruction is continuously updated iteratively. Finally, the panchromatic measurement is used as the guidance image, and the reconstruction result is optimized by guide filtering. A large number of experimental results demonstrate that our method without training data can reconstruct spectral data with both high spectral accuracy and spatial resolution.

Multi-angle lensless ptychographic imaging via adaptive correction and the Nesterov method.

Lensless systems based on ptychographic imaging can simultaneously achieve a large field of view and high resolution while having the advantages of small size, portability, and low cost compared to traditional lensed imaging. However, lensless imaging systems are susceptible to environmental noise and have a lower resolution of individual images than lens-based imaging systems, which means that they require a longer time to obtain a good result. Therefore, in this paper, to improve the convergence rate and robustness of noise in lensless ptychographic imaging, we propose an adaptive correction method, in which we add an adaptive error term and noise correction term in lensless ptychographic algorithms to reach convergence faster and create a better suppression effect on both Gaussian noise and Poisson noise. The Wirtinger flow and the Nesterov algorithms are used in our method to reduce computational complexity and improve the convergence rate. We applied the method to phase reconstruction for lensless imaging and demonstrated the effectiveness of the method by simulation and experiment. The method can be easily applied to other ptychographic iterative algorithms.

Efficient multiplexed illumination and imaging approach for Fourier ptychographic microscopy.

A Fourier ptychographic microscope (FPM) can obtain images with high resolution and a wide field of view (FOV). However, the time-consuming process of image acquisition and computation leads to low reconstruction efficiency. Therefore, we propose a state-multiplexed method through an optimized illumination pattern to accelerate FPM. First, to reduce the redundancy of the spectrum and analyze the impact of missing a certain sub-spectrum on overall spectrum reconstruction, we use an image quality evaluation method to obtain the differential expression between missing a certain LED lighting and all LED lighting. Second, we use the difference expression to select the important LEDs and obtain an optimized illumination pattern that lights up only the LEDs in the central area and the edge LEDs. Then, we update the multiplexing method with the new, to the best of our knowledge, illumination pattern and obtain satisfactorily reconstructed images. Finally, we validate the effectiveness and efficiency of our method with both simulation and experiments. Compared with the traditional method, our method accelerates the reconstruction speed of FPM while ensuring a large FOV and high resolution, saving about 73% of time.

An efficient Fourier ptychographic microscopy method based on optimized pattern of LED angle illumination.

Considering angle diversity and synthetic aperture, Fourier ptychographic microscopy (FPM) could address contradiction of high resolution and wide field of view. However, in the conventional FPM method, large capture quantity leads to poor efficiency. So, an efficient FPM method based on optimized pattern of LED angle illumination is proposed. Firstly, from position relationship between the LED, aperture and sample, we obtain the theoretical expandable spectrum range and the spectrum distribution in Fourier space of all LEDs. Secondly, we u se image quality assessment methods to extract differential expressions between an arbitrary LED illumination and full LEDs illumination. Thirdly, the optimized angle illumination strategy is achieved according to the analysis of different expressions. Based on this method, we design a rhombus-based illumination method for the LED array to accelerate the FPM efficiency. Finally, we validate the effectiveness and efficiency of our method with both simulated and real experiments. The results indicate that our method can effectively improve the efficiency for FPM without sacrificing image reconstruction quality.

Fine-time measurement circuit based on 180° phase-shifted clock sampling in time-to-digital converters.

Dead time is an important parameter in time-to-digital converters, which is the significant time measure circuit. To reduce the dead time, this note proposes a new fine-time measurement circuit. In this configuration, two clocks having a phase difference of 180° are used to sample the signals passing through the delay chain, and their average is taken as the final measurement result. The experimental results demonstrate that the dead time of the proposed circuit is observed to be 1.25 ns at 800 MHz clock and the circuit logic resource consumption of the proposed circuit is reduced by approximately 25% compared to other methods.

Microscopic imaging quality improvement through L0 gradient constraint model based on multi-fields of view analysis.

The degradation of optical microscopic imaging is space-variant, and how to fast restore optical degraded image remains a special problem. Based on point spread function (PSF) estimation under each field of view (FOV), a L0 gradient-constrained image restoration method is proposed to solve optical degradation in microscopic imaging. Firstly, the whole scene is segmented into several different regions according to different FOV. The PSFs for each region are estimated from modulation transfer function (MTF) measured in advance. Secondly, a penalty function is designed using L0 gradient constraint to deblur the degraded images of each sub-FOV. Finally, a weighted stitching approach is used to stitch the restored images of multiple FOV (m-FOV). Experimental results indicate that the m-FOV analysis could well solve the problem of space-variant degradation. Compared with the other methods, both subjective and objective evaluation results prove that the L0 norm idea could rapidly and effectively restore the degraded image. The approach could be well applied to a real product.