Spectral Estimation of Digital Signals by the Orthogonal Kravchenko Wavelets $\{\widetilde{h_{a}(t)}\}$

Abstract

In this article, the approach based on the orthogonal Kravchenko wavelets { } () a h t is proposed. There is shown that obtained structures have some advantages in comparison with spectral wave analysis of ultra wideband (UWB) signals that are widely used in the remote sensing. This approach based on application of wavelets as spectral kernels is considered in the problems of digital UWB signal processing. In communication theory, the signals are represented in the form of linear combination of elementary functions. Application of spectral analysis of UWB signals in basis of digital functions in comparison with the spectral harmonious analysis gives certain advantages which are defined by the physical nature of the signal representation. Optimal processing in spectral area in comparison with the time possesses has advantages on application of numerical algorithms. The physical characteristics and analysis of numerical experiment confirm efficiency of new wavelets in the spectral estimation and digital UWB signal processing.

Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data

“…In this work, we employ the following noise-based measures to compare the fused images before and after adding the distortion (noise): mean squared error (MSE) (Karathanassi et al, 2007), root mean squared error (RMSE) (Karathanassi et al, 2007), peak signal to signal noise ratio (PSNR) (Damera-Venkata et al, 2000;Canga et al, 2005), mean absolute error (MAE) (Kravchenko et al, 2009); signal to noise ratio (SNR) (Damera-Venkata et al, 2000); universal index quality image (UIQI) (Wang and Bovik, 2002;Karathanassi et al, 2007), and enhancement measurement error (EME) (Taric et al, 2009). The mathematical expressions of these measures are summarized in Table 3.…”

Reconstruction of three-dimensional objects using video sequences

“…[3][4][5]. Most of these techniques have not efficient computational cost when we use them in real time applications [6]. Several drawbacks of the anaglyph implementation process such as losses of color perception and discomfort in prolonged viewing are exist [7].…”

Reconstruction and super-resolution algorithms of 2D-3D images and objects

“…Recently, WAFs have demonstrated their successful performance in the diverse fields, such as windowing in radar processing, compression and recognition of medical images, speech reconstruction, etc. [12]. The promising successful usage of the WT in reconstruction of the HR video can be justified by such a proposition:…”

“…The proposed SR approach takes into account the spatial and spectral WT pixel information that permits to reconstruct different video composition and texture nature, presenting good performance in terms of objective and subjective criteria. Current proposal employs the Wavelets based on atomic functions (WAF [12]). Recently, WAFs have demonstrated their successful performance in the diverse fields, such as windowing in radar processing, compression and recognition of medical images, speech reconstruction, etc.…”

“…During statistical simulations, five video sequences that are widely used in the experiments: Plant, Walter, Toy (256x256 pixels, 8 bits), Stephan and Flowers (352 x 240 pixels, 8 bits) were investigated. To evaluate different SR algorithms and compare their performances against the performance of reference SR techniques the criteria: PSNR, MAE, and NCD (in L* u * V· space) are used [12]. The reconstruction of initial sizes was realized applying different techniques explained above to form HR images, and fmally, comparing the original and HR image.…”