For hyperspectral image research, spectral characteristic retainment is more important than the spatial details retainment, so it is necessary to evaluate the spectral influence of hyperspectral image compressed sensing. In this paper, the researchers select a hyperspectral remote sensing image PROBE CHRIS with abundant coastal wetland ground objects to analyze spectral fidelity of wavelet tran...

Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence learning algorithm (CD), an approximation to the gradient of the data log-likelihood. A simple reconstruction error is often used as a stopping criterion for CD, although several authors [1], [2] have raised ...

Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. The goal is to minimize any structural errors in the reconstruction that could have a negative impact on its diagnostic quality. To this end, we propose a deep error correction network (DECN) for CSMRI. The DECN model consists of three part...

The quality of attractor reconstruction using the method of delays is known to be sensitive to the delay parameter, τ . Here we develop a new, computationally efficient approach to choosing τ that quantifies reconstruction expansion from the identity line of the embedding space. We show that reconstruction expansion is related to the concept of reconstruction signal strength and that increased ...

introduction: the purpose of this study was to compare the effect of a whole body vibration training (wbvt) protocol with a conventional physiotherapy (pt) program on knee joint position sense and balance after anterior cruciate ligament (acl) reconstruction. methods: the study was designed as a single blind rct, twenty athletes with unilateral acl reconstruction were randomly assigned to the w...

We propose using the Wasserstein loss for training in inverse problems. In particular, we consider a learned primal-dual reconstruction scheme for ill-posed inverse problems using the Wasserstein distance as loss function in the learning. This is motivated by miss-alignments in training data, which when using standard mean squared error loss could severely degrade reconstruction quality. We pro...

A new combinational window family for the design of prototype FIR filter of two-channel Quadrature Mirror Filter (QMF) bank is introduced. One variable window, viz., Kaiser Window is also used to design prototype filters. The design equations of variable window function based filter banks is also given in this article. Reconstruction error, which is used as an objective function, is minimized b...

A novel approach to image compression using vector quantization of linear (one-step) prediction errors is presented in this paper. In order to minin,ize the image reconstruction error, we choose the optimum predictor coefficients (in a least-squares sense) that satisfy the additional constraint that the energy of the impulse response function of the inverse reconstruction filter is bounded by a...

This paper presents a new approach to generate view-independent global illumination solution using kernel density estimation. Kernel density estimation allows smooth reconstruction of the radiance from hit points generated by shooting random walk or photon tracing. The advantage of this method is that an unbiased Monte-Carlo algorithm simulates light transport and that light reconstruction intr...

We consider non-ideal sampling and reconstruction schemes in which the sampling and reconstruction spaces as well as the input signal can be arbitrary. To obtain a good reconstruction of the signal in the reconstruction space from arbitrary samples, we suggest processing the samples prior to reconstruction with a linear transformation that is designed to minimize the worst-case squared-norm err...