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Fourier ptychographic microscopy (FPM) is a technique to reconstruct a high-resolution image from a set of low-resolution images captured with different illumination angles, which is susceptible to ambient noise, system noise, and weak currents when acquiring large-angle images, especially dark field images. To effectively address the noise problem, we propose an adaptive denoising algorithm based on a LED-based temporal variant noise model. Taking the results of blank slide samples as the reference value of noise, and analyzing the distribution of noise, we establish a statistical model for temporal variant noise, describing the relationship between temporal noise and LED spatial location. Based on this model, Gaussian denoising parameters are selected to adaptively denoise the images with different locations, with which high-resolution images can be reconstructed. Compared with other methods, the experimental results show that the proposed method effectively suppresses the noise, recovers more image details, increases the image contrast, and obtains better visual effects. Meanwhile, better objective evaluation also mirrors the advantages of the proposed algorithms.
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The research of scattering imaging is of great significance to the development of various fields, but the existing scattering imaging methods are difficult to combine for the advantages of non-invasiveness, real-time imaging, and high quality. In this paper, a new, to our knowledge, scattering imaging technique is proposed that optimizes the traditional autocorrelation imaging technique by multi-stage complexity guidance and the initial acceleration module. We introduce the complexity difference index into the phase iterative recovery step for effective complexity guidance, and add the initial module based on error-reduction iteration to realize a fast startup. A series of experiments is carried out to test the performance of the new technique. The results show that the proposed technique significantly improves the scattering reconstruction speed. Meanwhile, the accuracy and clarity of the reconstructed image are significantly higher than the traditional method in terms of fast imaging. Moreover, this technique has better robustness to noise compared to the traditional autocorrelation imaging technique. The experimental code for this paper is available on GitHub.
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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.
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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.
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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.
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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.
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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.
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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.
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Iluminação , Microscopia , Simulação por Computador , Análise de Fourier , Microscopia/métodosRESUMO
Since signal-dependent noise in a local weak texture region of a noisy image is approximated as additive noise, the corresponding noise parameters can be estimated from a given set of weakly textured image blocks. As a result, the meticulous selection of weakly textured image blocks plays a decisive role to estimate the noise parameters accurately. The existing methods consider the finite directions of the texture of image blocks or directly use the average value of an image block to select the weakly textured image block, which can result in errors. To overcome the drawbacks of the existing methods, this paper proposes a novel noise parameter estimation method using local binary cyclic jumping to aid in the selection of these weakly textured image blocks. The texture intensity of the image block is first defined by the cumulative average of the LBCJ information in the eight neighborhoods around the pixel, and, subsequently, the threshold is set for selecting weakly textured image blocks through texture intensity distribution of the image blocks and inverse binomial cumulative function. The experimental results reveal that the proposed method outperforms the existing alternative algorithms by 23% and 22% for the evaluative measures of MSE (a) and MSE (b), respectively.
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Algoritmos , Distribuição NormalRESUMO
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.
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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.
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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.