Iterative Method for Blind Evaluation of Mixed Noise Characteristics on Images

A new method for blind estimation of mixed noise parameters is proposed. The method is based on line fitting into a set of cluster centers obtained from scatter-plot of local variance and mean estimates. Improved estimation of cluster centers is performed on basis of fourth-order statistical moment analysis. The estimation results for the proposed method are compared to the results for other known methods using images from TID2008 database. It is shown that the proposed method provides estimation accuracy comparable to the estimation accuracy of the method based on maximum likelihood estimation of image and noise characteristics (which is considered the best among the existing methods). An advantage of our method is that it is considerably faster

Methods for Blind Estimation of Speckle Variance in SAR Images: Simulation Results and Verification for Real-Life Data

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Automatic Adaptive Lossy Compression of Multichannel Remote Sensing Images

In this chapter, we consider lossy compression of multichannel images acquired by remote sensing systems. Two main features of such data are taken into account. First, images contain inherent noise that can be of different intensity and type. Second, there can be essential correlation between component images. These features can be exploited in 3D compression that is demonstrated to be more efficient than component-wise compression. The benefits are in considerably higher compression ratio attained for the same or even less distortions introduced. It is shown that important performance parameters of lossy compression can be rather easily and accurately predicted

Blind evaluation of noise characteristics in multi-channel images exploiting inter-channel correlation

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On Noise Properties in Hyperspectral Images

International audienceWe focus on considering noise properties in hyperspectral images acquired by different sensors. An initial assumption is that signal-dependent and signal-independent components are present. Using modern methods of blind estimation of noise parameters from images at hand, contributions of signal-dependent and signal-independent noise components are evaluated and compared for real-life images. It is demonstrated that for some sub-bands, contribution of signal-independent components is prevailing whilst for other sub-band images, the situation is the opposit