Images are one of vital ways to get information for us. However, in the practical application, images
are often subject to a variety of noise, so that solving the problem of image denoising becomes
particularly important. The K-SVD algorithm can improve the denoising effect by sparse coding atoms
instead of the traditional method of sparse coding dictionary. In order to further improve the effect of
denoising, we propose to extended the K-SVD algorithm via group sparse representation. The key point
of this method is dividing the sparse coefficients into groups, so that adjusts the correlation among the
elements by controlling the size of the groups. This new approach can improve the local constraints
between adjacent atoms, thereby it is very important to increase the correlation between the atoms. The
experimental results show that our method has a better effect on image recovery, which is efficient to
prevent the block effect and can get smoother images.
A new image fusion approach based on the modeling of shearlet coefficients with normal inverse gaussian model is
proposed. The approach focus on the fusion of noisy images. Based on the statistical model and additive white gaussian
noise, an subband adaptive shrinkage function is derived by using the maximum a posteriori rule. And then, the new
scheme for shearlet-domain image fusion is proposed by incorporating the adaptive shrinkage rule into the fusion scheme.
Experimental results show the proposed method perfor very well with noisy images, outperform other conventional
L. Sendur and I. W. Selesnick suggest four jointly non-Gaussian bivariate models to characterize the dependency between a coefficient and its parent, and respectively derive the corresponding MAP estimators based on noisy wavelet coefficients in detail in . Among the four models, the second is a mixture model and it is quite complicated to evaluate parameters, so L. Sendur and I.W. Selesnick didn't give a concrete method. In this letter, a concrete mixture bivariate model will be described by drawing inspiration from Model 2. Expectation maximization (EM) algorithm is employed to find the parameters of new model. The simulation results show that the values of PSNR have a bit improvement compared with Model 1. The results can be viewed as a supplementary of model 2 in .
Speckle is a multiplicative noise that degrades ultrasound images. In this paper, a statistical spatially adaptive approach
for speckle reduction in medical ultrasound images based posterior conditional means (PCM) estimation in the
nonsubsampled contourlet domain is proposed. In this framework, a new class of statistical model for nonsubsampled
contourlet coefficients is proposed. And the proposed method uses the Gaussian distribution for speckle noise and
normal inverse Gaussian distribution for modeling the statistics of nonsubsampled contourlet coefficients in a
logarithmically transformed ultrasound images. Experiments are carried out using synthetically speckled and real
ultrasound images. The experimental results demonstrate that the proposed method performs better than several other
existing methods in terms of quantitative performance as well as in term of visual quality of the images.
In this paper, we proposed a SAR speckle reduction method based on sparse and redundant representations over
multiscale ridgelet dictionary. Firstly, the multiscale ridgelet function is proposed. And then based on it, the multiscale
ridgelet dictionary is constructed, which can sparsely represent the SAR images. Finally, we propose a global image
prior that forces sparsity over small patches in every location in the image. We define a maximum a-posteriori
probability (MAP) estimator as the minimizer of a well-defined global penalty term. The speckle reduction leads to a
simple iterated patch-by-patch sparse coding and averaging algorithm. The experimental results demonstrate that the
proposed method performs better than several other existing methods in terms of quantitative performance as well as in
term of visual quality of the images.
Audio data hiding is an important branch of information hiding technology. In this paper, a novel digital audio data
hiding scheme, which hides secret message into audio signals, including telephone speech, wideband speech, and
wideband audio, is proposed. The advantage of this scheme is its fully utility of the perceptual masking effect of Human
Audio System (HAS). Before the secret message embedded into the host signal, it is modulated according to the
perceptual masking characteristic of the host signal. Therefore, it is not easily to detect the hiding message in it. This
scheme also uses the cryptographic techniques before hiding message into host audio signal to ensure security.
Moreover, the extraction of the desired message does not need the host audio signal because of the use of the pseudorandom
sequence. Experimental results show that the embedded audio signal is not easily detected and the bit error of the
blind extracted message is small.
This paper describes a novel approach to multisensor image fusion using a new mathematical transform: the curvelet
transform. The transform has shown promising results over wavelet transform for 2-D signals. Wavelets, though well
suited to point singularities have limitation with orientation selectivity, and therefore, do not represent two-dimensional
singularities (e.g. smooth curves) effectively. Curvelet improves wavelet by incorporating a directional component. This
paper employs the curvelet transform for image fusion. Based on the local energy of direction curvelet subbands, we give
the definition of local band-limited contrast and use it as one of the fusion rules. The local band-limited contrast can
reflect the response of local image features in human visual system truly. When used to image fusion in noiseless
circumstance, it is effective. But in noisy circumstance, it is not always robust. According to the different characteristics
between image features and noise, the local directional energy entropy is proposed. It can distinguish the noise and local
image features. In this paper, the combination of local band-limited contrast and local directional energy entropy is used
as image fusion. Experimental results show that it is robust in noisy and noiseless image fusion system.