We have developed an image quality theory for reconstruction that we apply to filtered back-projection (FBP) and statistical reconstruction (OSEM) for Single Photon Emission Computed Tomography (SPECT). Quantitative measures of reconstruction performance are given in terms of signal and noise power spectra, SPS and NPS, that we derive from phantom images. This allows evaluating the properties o...

In this paper, we will demonstrate three-dimensional tomographic reconstruction of space-borne highresolution SAR data using Shanghai as our test site. The high density of high-rise buildings in Shanghai leads to a rather complicated backscattering regime, which is difficult to handle with conventional interferometric processing. For the tomographic signal reconstruction, we use three different...

Approximate message passing (AMP) is a class of low-complexity scalable algorithms for solving high-dimensional linear regression tasks where one wishes to recover an unknown signal β0 from noisy, linear measurements y = Aβ0 + w. AMP has the attractive feature that its performance (for example, the mean squared error of its estimates) can be accurately tracked by a simple, scalar iteration refe...

A common distortion in videos is image instability in the form of chaotic global and local displacements of image frames caused by camera instability, fluctuations in the refraction index of the light propagation media and similar factors. Such videos that very frequently present moving objects on a stable background contain tremendous redundancy that potentially can be used for image stabiliza...

Audio signal recovery is a common problem in digital audio restoration field, because of corrupted samples that must be replaced. In this paper a subbands architecture is presented for audio signal recovery, using neural nonlinear prediction based on adaptive spline neural networks. The experimental results show the mean square reconstruction error, and maximum error obtained with increasing ga...

We discuss in this paper the feature extraction and signal reconstruction of air and bone conduction voices in the time domain by applying the independent component analysis (ICA). The basis functions of the air and bone conduction voices extracted by applying ICA are shown first, and then the quality of the signal reconstruction by using several of those basis functions is investigated from th...

We study compressed sensing (CS) signal reconstruction problems where an input signal is measured via matrix multiplication under additive white Gaussian noise. Our signals are assumed to be stationary and ergodic, but the input statistics are unknown; the goal is to provide reconstruction algorithms that are universal to the input statistics. We present a novel algorithm that combines: (i) the...

Quantitative diffusion imaging is a powerful technique for the characterization of complex tissue microarchitecture. However, long acquisition times and limited signal-to-noise ratio represent significant hurdles for many in vivo applications. This article presents a new approach to reduce noise while largely maintaining resolution in diffusion weighted images, using a statistical reconstructio...

Forward scatter radar (FSR), as a specially configured bistatic radar, is provided with the capabilities of target recognition and classification by the Shadow Inverse Synthetic Aperture Radar (SISAR) imaging technology. This paper mainly discusses the reconstruction of radio holographic signal (RHS), which is an important procedure in the signal processing of FSR SISAR imaging. Based on the an...

When the short-time Fourier transform (STFT) of an audio signal is arbitrarily modified, it no longer truly represents a time-domain signal. Classically, the accepted solution to obtain a time-domain signal from a modified STFT (MSTFT) is to invert the MSTFT to a time-domain signal that has an STFT that is closest to the MSTFT in a least squares sense. This is also the approach currently taken ...