Improved Sparse Signal Recovery via Adaptive Correlated Noise Model

acceptedVersionPeer reviewe

Joint Nonlocal, Spectral, and Similarity Low-Rank Priors for Hyperspectral-Multispectral Image Fusion

The fusion of a low-spatial-and-high-spectral resolution hyperspectral image (HSI) with a high-spatial-and-low-spectral resolution multispectral image (MSI) allows synthesizing a high-resolution image (HRI), supporting remote sensing applications, such as disaster management, material identification, and precision agriculture. Unlike existing variational methods using low-rank regularizations separately, we present an HSI-MSI fusion method promoting various low-rank regularizations jointly. Our method refines the HRI spatial and spectral correlations from the individual HSI and MSI data through the proper plug-and-play (PnP) of a nonlocal patch-based denoiser in the alternating direction method of multipliers (ADMM). Notably, we consider the nonlocal self-similarity, the spectral low-rank, and introduce a rank-one similarity prior. Furthermore, we demonstrate via an extensive empirical study that the rank-one similarity prior is an inherent characteristic of the HRI. Simulations over standard benchmark datasets show the effectiveness of the proposed HSI-MSI fusion outperforming state-of-the-art methods, particularly in recovering low-contrast areas.acceptedVersionPeer reviewe

Structural texture similarity metric based on intra-class variances

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Feed-forward neural network as nonlinear dynamics integrator for supercontinuum generation

The nonlinear propagation of ultrashort pulses in optical fibers depends sensitively on the input pulse and fiber parameters. As a result, the optimization of propagation for specific applications generally requires time-consuming simulations based on the sequential integration of the generalized nonlinear Schrödinger equation (GNLSE). Here, we train a feed-forward neural network to learn the differential propagation dynamics of the GNLSE, allowing emulation of direct numerical integration of fiber propagation, and particularly the highly complex case of supercontinuum generation. Comparison with a recurrent neural network shows that the feed-forward approach yields faster training and computation, and reduced memory requirements. The approach is generic and can be extended to other physical systems.acceptedVersionPeer reviewe

Noise modeling and variance stabilization of a computed radiography (CR) mammography system subject to fixed-pattern noise

In this work we model the noise properties of a computed radiography (CR) mammography system by adding an extra degree of freedom to a well-established noise model, and derive a variance-stabilizing transform (VST) to convert the signal-dependent noise into approximately signal-independent. The proposed model relies on a quadratic variance function, which considers fixed-pattern (structural), quantum and electronic noise. It also accounts for the spatial-dependency of the noise by assuming a space-variant quantum coefficient. The proposed noise model was compared against two alternative models commonly found in the literature. The first alternative model ignores the spatial-variability of the quantum noise, and the second model assumes negligible structural noise. We also derive a VST to convert noisy observations contaminated by the proposed noise model into observations with approximately Gaussian noise and constant variance equals to one. Finally, we estimated a look-up table that can be used as an inverse transform in denoising applications. A phantom study was conducted to validate the noise model, VST and inverse VST. The results show that the space-variant signal-dependent quadratic noise model is appropriate to describe noise in this CR mammography system (errors< 2.0% in terms of signal-to-noise ratio). The two alternative noise models were outperformed by the proposed model (errors as high as 14.7% and 9.4%). The designed VST was able to stabilize the noise so that it has variance approximately equal to one (errors< 4.1%), while the two alternative models achieved errors as high as 26.9% and 18.0%, respectively. Finally, the proposed inverse transform was capable of returning the signal to the original signal range with virtually no bias.acceptedVersionPeer reviewe

VizieR Online Data Catalog: 1Jy northern AGN sample (Planck+, 2016)

The complete sample presented in this paper consists of 104 northern and equatorial radio-loud AGN. It includes all AGN with declination >=-10° that have a measured average radio flux density at 37GHz exceeding 1Jy. Most of the sample sources have been monitored at Metsahovi Radio Observatory for many years, and the brightest sources have been observed for up to 30yr. (1 data file)

Pointwise Shape-Adaptive DCT Image Filtering and Signal-Dependent Noise Estimation

When an image is acquired by a digital imaging sensor, it is always degraded by some noise. This leads to two basic questions: What are the main characteristics of this noise? How to remove it? These questions in turn correspond to two key problems in signal processing: noise estimation and noise removal (so-called denoising). This thesis addresses both abovementioned problems and provides a number of original and effective contributions for their solution. The first part of the thesis introduces a novel image denoising algorithm based on the low-complexity Shape-Adaptive Discrete Cosine Transform (SA-DCT). By using spatially adaptive supports for the transform, the quality of the filtered image is high, with clean edges and without disturbing artifacts. We further present extensions of this approach to image deblurring, deringing and deblocking, as well as to color image filtering. For all these applications, the proposed SA-DCT approach demonstrates a state-of-the-art filtering performance, which is achieved at a very competitive computational cost. The second part of the thesis addresses the problem of noise estimation. In particular, we consider noise estimation for raw-data, i.e. the unprocessed digital output of the imaging sensor. We introduce a method for nonparametric estimation of the standard-deviation curve which can be used with non-uniform targets under non-uniform illumination. Thus, we overcome key limitations of the existing approaches and standards, which typically assume the use of specially calibrated uniform targets. Further, we propose a noise model for the raw-data. The model is composed of a Poissonian part, for the photon sensing, and a Gaussian part, for the remaining stationary disturbances in the output data. The model explicitly takes into account the clipping of the data, faithfully reproducing the nonlinear response of the sensor when parts of the image are over- or under-exposed. This model allows for the parametric estimation of the noise characteristics from a single image. For this purpose, a fully automatic algorithm is presented. Numerous experiments with synthetic as well as with real data are presented throughout the thesis, proving the efficiency of the proposed solutions. Finally, illustrative examples, which show how the methods proposed in the first and in the second part can be integrated within a single procedure, conclude the thesis