Proc. SPIE. 8768, International Conference on Graphic and Image Processing (ICGIP 2012)
KEYWORDS: Signal to noise ratio, Image compression, Receivers, Computer programming, Discrete wavelet transforms, Personal digital assistants, Image transmission, Bismuth, Error control coding, Rubidium
The SPIHT (set partitioning in hierarchical trees) algorithm, which works with the aid of the discrete wavelet
transform (DWT), is one of the most renowned techniques for image compression. One problem with SPIHT image
coding, especially when image transmission is involved, is that even just as few as one error in the embedded code
sequence can render the decoded image completely unrecognizable. In this paper, we propose a scheme in which an
SPIHT code sequence is packetized into two types: CP (critical packet) and RP (refinement packet). Then, we address
the transmission of those SPIHT packets over erasure channels. The basic idea is to provide unequal error protection
(UEP) to those packets by diversity (i.e., repeated transmissions) so that, at the receiver, the decoded image quality
(measured in the expected SNR, signal-to-noise-ratio) is as good as possible. A diversity allocation (DA) algorithm,
referred to as progressive diversity allocation (PDA), is proposed. It works in a fashion and is naturally compatible with
the progressive transmission of SPIHT-encoded images. Experiments show that the PDA scheme produces good results,
nearly as good as achieved by the method of full search.
In combination with the discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) is one of the
most renowned techniques for image compression. One problem with SPIHT, however, is that its decoding can be
extremely sensitive to errors in its embedded bit stream. In this paper, we address the issue of transmitting SPIHTencoded
images in packets via erasure channels, wherein each received packet is either error-free or totally discarded. In
our scheme, the original SPIHT code sequence is packetized (into two types: critical packet (CP) and refinement packet
(RP)). Those packets are then allocated with different diversity orders for unequal error protection (UEP) so that the
decoded image is expected to attain the highest signal-to-noise ratio (SNR) at the receiver. Simulations show that the
proposed scheme significantly improves the decoded images as compared to the case of no diversity allocation and the
case of not distinguishing between CP and RP.
In the past decade, wavelet filters have been widely applied to signal processing. In effect, wavelet filters are perfect reconstruction filter banks (PRFBs). However, in most researches, the filterbanks and wavelets operate on real- valued or complex-valued signals. In this paper, PRFBs operating over integer quotient rings (IQRs) are introduced. We denote an IQR as Z/(q). Algorithms for constructing such filter banks are proposed. The PRFB design can be carried out either in the time or the frequency domain. We demonstrate that some classical or well known filter tap coefficients can even be transformed into values over Z/(q) in a simple and straightforward way. Here we emphasize that to achieve perfect reconstruction (PR), the filters need not to work on elements in fields. In fact, operating on elements in IQRs can achieve PR with proper choices of a ring and filter tap coefficients. The designed filter banks can be orthogonal or biorthogonal. Based ona PRFB over an IQR, to which we refer as an IQR-PRFB, a perfect reconstruction transmultiplexer (PRTM), to which we refer as an IQR-PRTM, can be derived. Through the utilization of the IQR-PRTM multiplexing and multiple access in a multi-user digital communication system can be realized. The IQR-PRTM effectively decomposes the communication signal space into several orthogonal subspaces, where each multiplexed user sends his message in one of them. If some of the orthogonal subspaces are preserved for parity check, then error correction at the receiving end can be performed. In the proposed schemes, the data to be transmitted must be represented with elements of Z/(q), which can be done easily. A modulation and demodulation/detection scheme, in conjunction with the IQR-PRTM is proposed.
Two images of the same scene are sometimes corrupted by noise and different portions of them are blurred differently. In this paper, we proposed to use the techniques of image fusion and image restoration to improve the quality of the images. The proposed algorithms are aided by image registration, which estimates the matching parameters between the images to be registered. They are scale factor, angle of rotation and difference of locations. However, for the saving of computation time in the registration process, we apply the wavelet transform to perform a multi-resolution registration. The gradient that aids the searching of the feature points between the images to be registered is computed from the derivative operator derived from wavelet theory. Experimental results show that we can align and register the two original images successfully. Then, with fusion or restoration, a clearer version of the scene than that of the original images is obtained.
The discrete wavelet transform performs multiresolution analysis, which effectively decomposes a digital image into components with different degrees of details. In practice, it is usually implemented in the form of filter banks. If the filter banks are cascaded and both the low-pass and the high-pass components are further decomposed, a wavelet packet is obtained. The coefficients of the wavelet packet effectively represent subimages in different resolution levels. In the energy-sorted wavelet- packet decomposition, all subimages in the packet are then sorted according to their energies. The most important subimages, as measured by the energy, are preserved and coded. By investigating the histogram of each subimage, it is found that the pixel values are well modelled by the Laplacian distribution. Therefore, the Laplacian quantization is applied to quantized the subimages. Experimental results show that the image coding scheme based on wavelet packets achieves high compression ratio while preserving satisfactory image quality.
In this paper we address some of the main shortcomings of multi-channel (MC) linear restoration filters. The problem of restoring a MC image and simultaneously estimating the MC power spectrum of the image and the noise, required by linear minimum mean squared error (LMMSE) filters is investigated, using the expectation-maximization (EM) algorithm. Second, the problem of estimating, the regularization parameters and operator, required by regularized least-squares (RLS) MC restoration filters is investigated using the cross-validation (CV) function. Furthermore, a novel representation of MC signal processing is introduced. This notation leads to a more natural extension of single-channel (SC) signal processing algorithms to the MC case and yields a new class of matrices which we call semi-block- circulant (SBC) matrices. The properties of these matrices are examined and a family of new efficient algorithms is developed for the computation of the MC EM and CV functions.
In this paper we propose two algorithms for the restoration of images based on the bispectrum. The bispectrum of a signal is the Fourier transform of its triple correlation. While second- order statistics (e.g., correlation function, power spectrum, etc.) do not provide any information about the phase of the signal, third-order statistics (e.g., triple correlation, bispectrum, etc.) allow the recovery of the phase of the signal. We propose two algorithms for estimating the magnitude and the phase of the image, where the ambiguity due to the use of the principal value of the phase component is taken into account. Image lines are used in our experiments to test the effectiveness of the proposed algorithms.
We propose algorithms for estimating the phase of a deterministic signal using its bispectrum. The bispectrum of a signal is the (discrete) Fourier transform of its triple correlation. While second-order statistics (e.g., correlation function, power spectrum, etc.) do not provide any information about the phase of the signal, third-order statistics (e.g., triple correlation, bispectrum, etc.) allow the recovery of the phase of the signal. We showthatthe applicability oftwo commonly used algorithms for phase estimation using the bispectrum is restricted to signals with simple phase characteristics. We propose algorithms for estimating the phase of arbitrary signals such as images, by taking into account the ambiguity due to the use of the principal value of the phase component. The resulting estimated phase is incorporated into a restoration filter. Image lines and images are used in our experiments to test the effectiveness of the proposed algorithms.
In this paper, the problem of identifying the image and blur parameters and restoring a noisy blurred image is addressed. Specifying the blurring process by its point spread function (PSF), the blur identification problem is formulated as the maximum likelihood estimation (MLE) of the PSF. Modeling the original image and the additive noise as zeromean
Gaussian processes, the MLE of their covariance matrices is also computed. An iterative approach, called the EM (expectation-maximization) algorithm, is used to find the maximum likelihood estimates ofthe relevant unknown parameters. In applying the EM algorithm, the original image is chosen to be part of the complete data; its estimate is computed in the
E-step of the EM iterations and represents the restored image. Two algorithms for identification/restoration, based on two different choices of complete data, are derived and compared. Simultaneous blur identification and restoration is performed by the first algorithm, while the identification of the blur results from a separate minimization in the second algorithm. Experiments with simulated and photographically blurred images