1,721,072 research outputs found
Noise modeling and estimation in image sequences from thermal infrared cameras
No full textIn this paper we present an automated procedure devised to measure noise variance and correlation from a sequence, either temporal or spectral, of digitized images acquired by an incoherent imaging detector. The fundamental assumption is that the noise is signal-independent and stationary in each frame, but may be non-stationary across the sequence of frames. The idea is to detect areas within bivariate scatterplots of local statistics, corresponding to statistically homogeneous pixels. After that, the noise PDF, modeled as a parametric generalized Gaussian function, is estimated from homogeneous pixels. Results obtained applying the noise model to images taken by an IR camera operated in different environmental conditions are presented and discussed. They demonstrate that the noise is heavy-tailed (tails longer than those of a Gaussian PDF) and spatially autocorrelated. Temporal correlation has been investigated as well and found to depend on the frame rate and, by a small extent, on the wavelength of the thermal radiation
Multiresolution MAP Despeckling of SAR Images Based on Locally Adaptive Generalized Gaussian pdf Modeling
No full textIn this paper, a new despeckling method based on undecimated wavelet decomposition and maximum a posteriori (MAP) estimation is proposed. Such a method relies on the assumption that the probability density function (pdf) of each wavelet coefficient is generalized Gaussian (GG). The major novelty of the proposed approach is that the parameters of the GG pdf are taken to be space-varying within each wavelet frame. Thus, they may be adjusted to spatial image context, not only to scale and orientation. Since the MAP equation to be solved is a function of the parameters of the assumed pdf model, the variance and shape factor of the GG function are derived from the theoretical moments, which depend on the moments and joint moments of the observed noisy signal and on the statistics of speckle. The solution of the MAP equation yields the MAP estimate of the wavelet coefficients of the noise-free image. The restored SAR image is synthesized from such coefficients. Experimental results, carried out on both synthetic speckled images and true SAR images, demonstrate that MAP filtering can be successfully applied to SAR images represented in the shift-invariant wavelet domain, without resorting to a logarithmic transformatio
Simplified MAP Despeckling Based on Laplacian-Gaussian Modeling of Undecimated Wavelet Coefficients
No full textA critical review of fusion methods for true colour display of very high resolution images of urban areas
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Regularization of optic flow estimates by means of weighted vector median filtering
No full textVector median filtering has been recently proposed as an effective method to refine estimated velocity fields. Here, the use of a weighted vector median filtering is suggested to improve the regularization of the optic flow field across motion boundaries. Information about the confidence of the estimated pixel velocities is exploited for the choice of the filter weights. Experimental results, on both synthetic and real-world sequences, show the effectiveness of the proposed procedur
MAP Despeckling of SAR Images Based on Local PDF Modeling in the Undecimated Wavelet Domain
No full textSegmentation-Based MAP Despeckling of SAR Images in the Undecimated Wavelet Domain
No full textIn this paper, a novel despeckling algorithm based on undecimated wavelet decomposition and maximum a posteriori estimation is proposed. Such a method represents an improvement with respect to the filter presented by the authors, and it is based on the same conjecture that the probability density functions (pdfs) of the wavelet coefficients follow a generalized Gaussian (GG) distribution. However, the approach introduced here presents two major novelties: 1) theoretically exact expressions for the estimation of the GG parameters are derived: such expressions do not require further assumptions other than the multiplicative model with uncorrelated speckle, and hold also in the case of a strongly correlated reflectivity; 2) a model for the classification of the wavelet coefficients according to their texture energy is introduced. This model allows us to classify the wavelet coefficients into classes having different degrees of heterogeneity, so that ad hoc estimation approaches can be devised for the different sets of coefficients. Three different implementations, characterized by different approaches for incorporating into the filtering procedure the information deriving from the segmentation of the wavelet coefficients, are proposed. Experimental results, carried out on both artificially speckled images and true synthetic aperture radar images, demonstrate that the proposed filtering approach outperforms the previous filters, irrespective of the features of the underlying reflectivit
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