Current Issue : July - September Volume : 2020 Issue Number : 3 Articles : 5 Articles
The augmented Lagrangian method (ALM) is one of the most successful first-order methods for convex programming with linear\nequality constraints. To solve the two-block separable convex minimization problem, we always use the parallel splitting ALM\nmethod. In this paper, we will show that no matter how small the step size and the penalty parameter are, the convergence of the\nparallel splitting ALM is not guaranteed. We propose a new convergent parallel splitting ALM (PSALM), which is the regularizing\nALMâ??s minimization subproblem by some simple proximal terms. In application this new PSALM is used to solve video\nbackground extraction problems and our numerical results indicate that this new PSALM is efficient....
Terahertz coded-aperture imaging (TCAI) has many advantages such as forward-looking\nimaging, staring imaging and low cost and so forth. However, it is difficult to resolve the target under\nlow signal-to-noise ratio (SNR) and the imaging process is time-consuming. Here, we provide an\nefficient solution to tackle this problem. A convolution neural network (CNN) is leveraged to develop\nan off-line end to end imaging network whose structure is highly parallel and free of iterations.\nAnd it can just act as a general and powerful mapping function. Once the network is well trained and\nadopted for TCAI signal processing, the target of interest can be recovered immediately from echo\nsignal. Also, the method to generate training data is shown, and we find that the imaging network\ntrained with simulation data is of good robustness against noise and model errors. The feasibility\nof the proposed approach is verified by simulation experiments and the results show that it has a\ncompetitive performance with the state-of-the-art algorithms....
Hyperspectral (HS) imaging has been used extensively in remote sensing applications\nlike agriculture, forestry, geology and marine science. HS pixel classification is an important task to\nhelp identify different classes of materials within a scene, such as different types of crops on a farm.\nHowever, this task is significantly hindered by the fact that HS pixels typically form high-dimensional\nclusters of arbitrary sizes and shapes in the feature space spanned by all spectral channels. This is\neven more of a challenge when ground truth data is difficult to obtain and when there is no reliable\nprior information about these clusters (e.g., number, typical shape, intrinsic dimensionality). In this\nletter, we present a new graph-based clustering approach for hyperspectral data mining that does not\nrequire ground truth data nor parameter tuning. It is based on the minimax distance, a measure of\nsimilarity between vertices on a graph. Using the silhouette index, we demonstrate that the minimax\ndistance is more suitable to identify clusters in raw hyperspectral data than two other graph-based\nsimilarity measures: mutual proximity and shared nearest neighbours. We then introduce the\nminimax bridgeness-based clustering approach, and we demonstrate that it can discover clusters of\ninterest in hyperspectral data better than comparable approaches....
Aiming at the problems that the strategy of target bit allocation at the CTU layer has deviations from the human subjective\nobservation mechanism, and the update phase of parametric model has a higher complexity in the JCTVC-K0103 rate control\nalgorithm of ITU-T H.265/high efficiency video coding (HEVC) standard. Optimized rate control (ORC) algorithm of ITU-T\nH.265/HEVC based on region of interest (ROI) is proposed. Firstly, the algorithm extracts the region of interest of video frames\nbased on time and space domains by using the improved Itti model. Then, the weight of target bits w is recalculated based on\nspace-time domains to improve the rate control accuracy, and the target bits are distributed based on ROI by the adaptive weight\nalgorithm once again to make the output videos more attuned with the human visual attention mechanism. Finally, the quasi-\nNewton algorithm is used to update the rate distortion model, which reduces the computational complexity in the update phase of\nthe parametric model. The experimental results show that the ORC algorithm can obtain a better subjective quality in the\ncompressed results with less bit error compared with the other two algorithms. Meanwhile, the rate distortion performance of the\nORC algorithm is better on the premise of guaranteeing rate control performance....
This paper presents five different statistical methods for ground scene prediction (GSP)\nin wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a\nreference image in a change detection algorithm yielding a high probability of detection and low\nfalse alarm rate. The predictions are based on image stacks, which are composed of images from the\nsame scene acquired at different instants with the same flight geometry. The considered methods\nfor obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean;\n(iii) median; (iv) intensity mean; and (v) mean...................
Loading....