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Interrogation of subcellular biological dynamics occurring in a living cell often requires noninvasive imaging of the fragile cell with high spatiotemporal resolution across all three dimensions. It thereby poses big challenges to modern fluorescence microscopy implementations because the limited photon budget in a live-cell imaging task makes the achievable performance of conventional microscopy approaches compromise between their spatial resolution, volumetric imaging speed, and phototoxicity. Here, we incorporate a two-stage view-channel-depth (VCD) deep-learning reconstruction strategy with a Fourier light-field microscope based on diffractive optical element to realize fast 3D super-resolution reconstructions of intracellular dynamics from single diffraction-limited 2D light-filed measurements. This VCD-enabled Fourier light-filed imaging approach (F-VCD), achieves video-rate (50 volumes per second) 3D imaging of intracellular dynamics at a high spatiotemporal resolution of ~180 nm × 180 nm × 400 nm and strong noise-resistant capability, with which light field images with a signal-to-noise ratio (SNR) down to -1.62 dB could be well reconstructed. With this approach, we successfully demonstrate the 4D imaging of intracellular organelle dynamics, e.g., mitochondria fission and fusion, with ~5000 times of observation. A super-resolution light-field imaging method enables video-rate 3D imaging of intracellular dynamics beyond diffraction limit and with strong noise-resistant capability. [ABSTRACT FROM AUTHOR]

Taking the advantages of photonic technology, such as large bandwidth, good tunability, and immunity to electromagnetic interference, a two-step high-accuracy photonics-assisted microwave frequency measurement method based on frequency-to-time mapping is proposed. Through high-speed optical frequency sweeping and optical filtering, the method maps different signal frequencies to time-domain pulses at different time and can measure multiple frequencies simultaneously. With the assistance of ultra-high-speed frequency sweeping and frequency-to-time mapping, the fast coarse identification of multiple different signal frequencies in the wide bandwidth range of 12 GHz is realized, and the measurement accuracy is better than 100 MHz. After the signal frequency is coarsely determined, low-speed frequency sweeping and frequency-to-time mapping are employed to realize accurate identification of the signal frequency within a small bandwidth. When the sweep rate is 2 and 0.2 GHz/μs, the accuracy of accurate measurement is better than 30 and 20 MHz respectively. Through the two-step measurement, high-accuracy frequency measurement can be achieved in a large bandwidth range while greatly reducing the microwave frequency measurement time. [ABSTRACT FROM AUTHOR]

Spectral signals often suffer from the common problems of band overlap and random Gaussian noise. To address these problems, we propose a sparsity regularization-based model that deconvolutes the degraded spectral signals. Sparsity regularization is achieved by fitting the probability density function of the gradient of the signal, and then, the iteratively reweighted least-squares method is used to solve the minimization problem. Results from experiments using real spectral signals showed that this algorithm separates the overlapping peaks and effectively suppresses the noise. The deconvoluted spectral signals will promote the practical application of infrared spectral analysis in the fields of target recognition, material identification, and chemometrics analysis. [ABSTRACT FROM AUTHOR]

Image restoration is an ill-posed problem that requires regularization to solve. Many existing regularization terms in the literature are the convex function. However, nonconvex nonsmooth regularization has advantages over convex regularization for restoring images, but its practical interest used to be limited by the difficulty of the computational stage which requires a nonconvex nonsmooth minimization. In this paper, an adaptive nonconvex nonsmooth regularization is proposed for image restoration by using the spatial information indicator. Moreover, an efficient numerical algorithm for solving the resulting minimization problem is provided by applying the variable splitting and the penalty techniques. Finally, its advantages are shown in deblurring edges and restoring fines of image simultaneously in experiments. [ABSTRACT FROM AUTHOR]

Total variation (TV) has been used as a popular and effective image prior model in regularization-based image restoration, because of its ability to preserve edges. However, as the total variation model favors a piecewise constant solution, the processing results in the flat regions of the image are poor, and the amplitude of the edges will be underestimated; the underlying cause of the problem is that the model is based on derivation which only considers the local feature of the image. In this paper, we first propose an adaptive non-local total variation image blind restoration algorithm for deblurring a single image via a non-local total variation operator, which exploits the correlation in the image, and then an extended split Bregman iteration is proposed to address the joint minimization problem. Second, the maximum average absolute difference (MAAD) method is employed to estimate the blur support and initialize the blur kernel. Extensive experiments demonstrate that the proposed approach produces results superior to most methods in both visual image quality and quantitative measures. [ABSTRACT FROM AUTHOR]

The definition of valuable training samples and automatic classification of land cover with remote sensing data are both classical problems, which are known to be difficult and have attracted major research efforts. In this paper, a method of modified K-means-based support vector machine (SVM) classification is proposed to use a hybrid sample selection that leverages the informativeness and representativeness of training samples to classify real multi/hyperspectral images. The hybrid sample selection (close-to-cluster-border sampling and near-cluster-center sampling) is constructed on the reduced convex hulls (RCHs) of clustering structure and can reduce the risk of overtraining caused by active sample selection of active learning methods. Numerical results obtained on the classification of three challenging remote sensing images (Landsat-7 ETM+, AVIRIS Indian pines, and KSC) by comparing the proposed technique with random sampling (RS) and margin sampling (MS) demonstrate the good efficiency and high accuracy of our approach. [ABSTRACT FROM AUTHOR]

Multichannel passive millimeter-wave (PMMW) imaging systems have advantage in operating speed, however traditional data processing methods for single channel systems are not applicable for such kind of system. This paper describes an 8-mm multichannel PMMW imaging system, points out the specific problems encountered in multichannel systems, and proposes an experimental multichannel calibration approach in microwave unreflected chamber. In the last superresolution algorithms considering imaging process and some related issues are discussed. The imaging result shows the proposed processing approaches are effective. [ABSTRACT FROM AUTHOR]