作者：
Wang, HuafeiWang, XianpengLan, XiangSu, TingHainan Univ
Sch Informat & Commun Engn Sch Ecol & Environm State Key Lab Marine Resource Utilizat South China Haikou 570228 Peoples R China Hainan Univ
State Key Lab Marine Resource Utilizat South China Haikou 570228 Peoples R China
Using deep learning (DL) to achieve direction-of-arrival (DOA) estimation is an open and meaningful exploration. Existing DL- based methods achieve DOA estimation by spectrum regression or multi- label classification ...
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Using deep learning (DL) to achieve direction-of-arrival (DOA) estimation is an open and meaningful exploration. Existing DL- based methods achieve DOA estimation by spectrum regression or multi- label classification task. While, both of them face the problem of off-grid errors. In this paper, we proposed a cascaded deep neural network (DNN) framework named as off-grid network (OGNet) to provide accurate DOA estimation in the case of off-grid. The OGNet is composed of an autoencoder consisted by fully connected (FC) layers and a deep convolutional neural network (CNN) with 2-dimensional convolutional layers. In the proposed OGNet, the off-grid error is modeled into labels to achieve off-grid DOA estimation based on its sparsity. As compared to the state-of-the-art grid- based methods, the OGNet shows advantages in terms of precision and resolution. The effectiveness and superiority of the OGNet are demonstrated by extensive simulation experiments in different experimental conditions.
We present a systematic approach to forward-motion-compensated predictive video coding. The first step is the definition of a flexible model that compactly represents motion fields, The inhomogeneity and spatial coher...
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We present a systematic approach to forward-motion-compensated predictive video coding. The first step is the definition of a flexible model that compactly represents motion fields, The inhomogeneity and spatial coherence properties of motion fields are captured using linear multiscale models. One possible design is based on linear finite elements and yields a multiscale extension of the triangle motion compensation (TMC) method, The second step is the choice of a computational technique that identifies the coefficients of the linear model, We study a modified optical how technique and minimize a cost function closely related to Horn and Schunck's criterion, The cost function balances accuracy and complexity of the motion-compensated predictor and is viewed as a measure of goodness of the motion field, It determines not only the coefficients of the model, but also the quantization method. We formulate the estimation and quantization problems jointly as a discrete optimization problem and solve it using a fast multiscale relaxation algorithm, A hierarchical extension of the algorithm allows proper handling of large displacements, Simulations on a variety of video sequences have produced improvements over TMC and over the half-pel-accuracy, full-search block matching algorithm, in excess of 0.5 dB in average, The results are visually superior as well, In particular, the reconstructed video is entirely free of blocking artifacts.
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