In this paper we propose an image super-resolution algorithm using wavelet-domain Hidden Markov Tree (HMT) model. Wavelet-domain HMT models the dependencies of multiscale wavelet Coefficients through the state probabilities of wavelet coefficients, whose distribution densities can be approximated by the Gaussian mixture. Because wavelet-domain HMT accurately characterizes the statistics of real...

Denoising of images corrupted by Gaussian noise using wavelet transform is of great concern in the past two decades. In wavelet denoising method, detail wavelet coefficients of noisy image are thresholded using a specific thresholding function by comparing to a specific threshold value, and then applying inverse wavelet transform, results in denoised image. Recently, an effective image denoisin...

The traditional continuous wavelet transform is plagued by the cone-ofinfluence, ie wavelets which extend past either end of a finite timeseries return transform coefficients which tend to decrease as more of the wavelet is truncated. These coefficients may be corrected simply by rescaling the remaining wavelet. The corrected wavelet transform displays no cone-of-influence and maintains reconst...

Wavelet based image denoising is an important technique in the area of image noise reduction. In this paper, a new adaptive wavelet based image denoising algorithm in the presence of Gaussian noise is developed. In the existing wavelet thresholding methods, the final noise reduced image has limited improvement. It is due to keeping the approximate wavelet coefficients unchanged. Since noise aff...

Watermark detection is a way of verifying the existence of a watermark in a watermarking scheme used for copyright protection of digital data. Statistical modeling of wavelet subband coefficients has been extensively used in watermark detection. The effectiveness of a watermarking scheme depends directly on how the wavelet coefficients are modeled. It is known that the vector-based hidden Marko...

Let sk (k = 1, . . . , K) denote the locations of theK = 17 temperature series. At each location k we observe M(sk) temperature values z(sk) = (z1(sk), . . . , zM(sk)(sk)) T drawn from the M(sk)-variate random vector (RV) Z(s). Suppose at location k that these observations correspond to times Tt(sk), for t = 1, . . . ,M(sk). Since all sampling in the MCMC algorithm is performed on the wavelet d...

In standard wavelet methods, the empirical wavelet coefficients are thresholded term by term, on the basis of their individual magnitudes. Information on other coefficients has no influence on the treatment of particular coefficients. We propose and investigate a wavelet shrinkage method that incorporates information on neighboring coefficients into the decision making. The coefficients are con...