Abstract: Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.
Abstract: In this paper, an innovative watermarking scheme for audio signal based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.
Abstract: Multimedia security is an incredibly significant area
of concern. A number of papers on robust digital watermarking have
been presented, but there are no standards that have been defined so
far. Thus multimedia security is still a posing problem. The aim of
this paper is to design a robust image-watermarking scheme, which
can withstand a different set of attacks. The proposed scheme
provides a robust solution integrating image moment normalization,
content dependent watermark and discrete wavelet transformation.
Moment normalization is useful to recover the watermark even in
case of geometrical attacks. Content dependent watermarks are a
powerful means of authentication as the data is watermarked with its
own features. Discrete wavelet transforms have been used as they
describe image features in a better manner. The proposed scheme
finds its place in validating identification cards and financial
instruments.
Abstract: In this paper, a new secure watermarking scheme for
color image is proposed. It splits the watermark into two shares using
(2, 2)- threshold Visual Cryptography Scheme (V CS) with Adaptive
Order Dithering technique and embeds one share into high textured
subband of Luminance channel of the color image. The other share
is used as the key and is available only with the super-user or the
author of the image. In this scheme only the super-user can reveal
the original watermark. The proposed scheme is dynamic in the sense
that to maintain the perceptual similarity between the original and the
watermarked image the selected subband coefficients are modified
by varying the watermark scaling factor. The experimental results
demonstrate the effectiveness of the proposed scheme. Further, the
proposed scheme is able to resist all common attacks even with strong
amplitude.
Abstract: The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of
signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we
present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In
addition, this paper will include two main R peak detection methods
by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on
Dyadic Wavelet Transform DyWT.
Abstract: A multi-agent system is developed here to predict
monthly details of the upcoming peak of the 24th solar magnetic
cycle. While studies typically predict the timing and magnitude of
cycle peaks using annual data, this one utilizes the unsmoothed
monthly sunspot number instead. Monthly numbers display more
pronounced fluctuations during periods of strong solar magnetic
activity than the annual sunspot numbers. Because strong magnetic
activities may cause significant economic damages, predicting
monthly variations should provide different and perhaps helpful
information for decision-making purposes. The multi-agent system
developed here operates in two stages. In the first, it produces twelve
predictions of the monthly numbers. In the second, it uses those
predictions to deliver a final forecast. Acting as expert agents, genetic
programming and neural networks produce the twelve fits and
forecasts as well as the final forecast. According to the results
obtained, the next peak is predicted to be 156 and is expected to
occur in October 2011- with an average of 136 for that year.
Abstract: The information revealed by derivatives can help to
better characterize digital near-end crosstalk signatures with the
ultimate goal of identifying the specific aggressor signal.
Unfortunately, derivatives tend to be very sensitive to even low
levels of noise. In this work we approximated the derivatives of both
quiet and noisy digital signals using a wavelet-based technique. The
results are presented for Gaussian digital edges, IBIS Model digital
edges, and digital edges in oscilloscope data captured from an actual
printed circuit board. Tradeoffs between accuracy and noise
immunity are presented. The results show that the wavelet technique
can produce first derivative approximations that are accurate to
within 5% or better, even under noisy conditions. The wavelet
technique can be used to calculate the derivative of a digital signal
edge when conventional methods fail.
Abstract: This paper presents a new fingerprint coding technique
based on contourlet transform and multistage vector quantization.
Wavelets have shown their ability in representing natural images that
contain smooth areas separated with edges. However, wavelets
cannot efficiently take advantage of the fact that the edges usually
found in fingerprints are smooth curves. This issue is addressed by
directional transforms, known as contourlets, which have the
property of preserving edges. The contourlet transform is a new
extension to the wavelet transform in two dimensions using
nonseparable and directional filter banks. The computation and
storage requirements are the major difficulty in implementing a
vector quantizer. In the full-search algorithm, the computation and
storage complexity is an exponential function of the number of bits
used in quantizing each frame of spectral information. The storage
requirement in multistage vector quantization is less when compared
to full search vector quantization. The coefficients of contourlet
transform are quantized by multistage vector quantization. The
quantized coefficients are encoded by Huffman coding. The results
obtained are tabulated and compared with the existing wavelet based
ones.
Abstract: This paper describes a new measuring algorithm for
three-dimensional (3-D) braided composite material .Braided angle is
an important parameter of braided composites. The objective of this
paper is to present an automatic measuring system. In the paper, the
algorithm is performed by using vcµ6.0 language on PC. An
advanced filtered algorithm for image of 3-D braided composites
material performs has been developed. The procedure is completely
automatic and relies on the gray scale information content of the
images and their local wavelet transform modulus maxims.
Experimental results show that the proposed method is feasible.
The algorithm was tested on both carbon-fiber and glass-fiber
performs.
Abstract: Because of excellent properties, people has paid more
attention to SPIHI algorithm, which is based on the traditional wavelet
transformation theory, but it also has its shortcomings. Combined the
progress in the present wavelet domain and the human's visual
characteristics, we propose an improved algorithm based on human
visual characteristics of SPIHT in the base of analysis of SPIHI
algorithm. The experiment indicated that the coding speed and quality
has been enhanced well compared to the original SPIHT algorithm,
moreover improved the quality of the transmission cut off.
Abstract: In this paper, we propose a fully-utilized, block-based 2D DWT (discrete wavelet transform) architecture, which consists of four 1D DWT filters with two-channel QMF lattice structure. The proposed architecture requires about 2MN-3N registers to save the intermediate results for higher level decomposition, where M and N stand for the filter length and the row width of the image respectively. Furthermore, the proposed 2D DWT processes in horizontal and vertical directions simultaneously without an idle period, so that it computes the DWT for an N×N image in a period of N2(1-2-2J)/3. Compared to the existing approaches, the proposed architecture shows 100% of hardware utilization and high throughput rates. To mitigate the long critical path delay due to the cascaded lattices, we can apply the pipeline technique with four stages, while retaining 100% of hardware utilization. The proposed architecture can be applied in real-time video signal processing.
Abstract: In this work a new method for low complexity
image coding is presented, that permits different settings and great
scalability in the generation of the final bit stream. This coding
presents a continuous-tone still image compression system that
groups loss and lossless compression making use of finite arithmetic
reversible transforms. Both transformation in the space of color and
wavelet transformation are reversible. The transformed coefficients
are coded by means of a coding system in depending on a
subdivision into smaller components (CFDS) similar to the bit
importance codification. The subcomponents so obtained are
reordered by means of a highly configure alignment system
depending on the application that makes possible the re-configure of
the elements of the image and obtaining different importance levels
from which the bit stream will be generated. The subcomponents of
each importance level are coded using a variable length entropy
coding system (VBLm) that permits the generation of an embedded
bit stream. This bit stream supposes itself a bit stream that codes a
compressed still image. However, the use of a packing system on the
bit stream after the VBLm allows the realization of a final highly
scalable bit stream from a basic image level and one or several
improvement levels.
Abstract: Wavelet transform or wavelet analysis is a recently
developed mathematical tool in applied mathematics. In numerical
analysis, wavelets also serve as a Galerkin basis to solve partial
differential equations. Haar transform or Haar wavelet transform has
been used as a simplest and earliest example for orthonormal wavelet
transform. Since its popularity in wavelet analysis, there are several
definitions and various generalizations or algorithms for calculating
Haar transform. Fast Haar transform, FHT, is one of the algorithms
which can reduce the tedious calculation works in Haar transform. In
this paper, we present a modified fast and exact algorithm for FHT,
namely Modified Fast Haar Transform, MFHT. The algorithm or
procedure proposed allows certain calculation in the process
decomposition be ignored without affecting the results.
Abstract: In this paper, we start by first characterizing the most
important and distinguishing features of wavelet-based watermarking
schemes. We studied the overwhelming amount of algorithms
proposed in the literature. Application scenario, copyright protection
is considered and building on the experience that was gained,
implemented two distinguishing watermarking schemes. Detailed
comparison and obtained results are presented and discussed. We
concluded that Joo-s [1] technique is more robust for standard noise
attacks than Dote-s [2] technique.
Abstract: In this paper, a watermarking algorithm that uses the wavelet transform with Multiple Description Coding (MDC) and Quantization Index Modulation (QIM) concepts is introduced. Also, the paper investigates the role of Contourlet Transform (CT) versus Wavelet Transform (WT) in providing robust image watermarking. Two measures are utilized in the comparison between the waveletbased and the contourlet-based methods; Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC). Experimental results reveal that the introduced algorithm is robust against different attacks and has good results compared to the contourlet-based algorithm.
Abstract: In 3D-wavelet video coding framework temporal
filtering is done along the trajectory of motion using Motion
Compensated Temporal Filtering (MCTF). Hence computationally
efficient motion estimation technique is the need of MCTF. In this
paper a predictive technique is proposed in order to reduce the
computational complexity of the MCTF framework, by exploiting
the high correlation among the frames in a Group Of Picture (GOP).
The proposed technique applies coarse and fine searches of any fast
block based motion estimation, only to the first pair of frames in a
GOP. The generated motion vectors are supplied to the next
consecutive frames, even to subsequent temporal levels and only fine
search is carried out around those predicted motion vectors. Hence
coarse search is skipped for all the motion estimation in a GOP
except for the first pair of frames. The technique has been tested for
different fast block based motion estimation algorithms over different
standard test sequences using MC-EZBC, a state-of-the-art scalable
video coder. The simulation result reveals substantial reduction (i.e.
20.75% to 38.24%) in the number of search points during motion
estimation, without compromising the quality of the reconstructed
video compared to non-predictive techniques. Since the motion
vectors of all the pair of frames in a GOP except the first pair will
have value ±1 around the motion vectors of the previous pair of
frames, the number of bits required for motion vectors is also
reduced by 50%.
Abstract: In the power quality analysis non-stationary nature
of voltage distortions require some precise and powerful analytical
techniques. The time-frequency representation (TFR) provides a
powerful method for identification of the non-stationary of the
signals. This paper investigates a comparative study on two
techniques for analysis and visualization of voltage distortions with
time-varying amplitudes. The techniques include the Discrete
Wavelet Transform (DWT), and the S-Transform. Several power
quality problems are analyzed using both the discrete wavelet
transform and S–transform, showing clearly the advantage of the S–
transform in detecting, localizing, and classifying the power quality
problems.
Abstract: In 1990 [1] the subband-DFT (SB-DFT) technique was proposed. This technique used the Hadamard filters in the decomposition step to split the input sequence into low- and highpass sequences. In the next step, either two DFTs are needed on both bands to compute the full-band DFT or one DFT on one of the two bands to compute an approximate DFT. A combination network with correction factors was to be applied after the DFTs. Another approach was proposed in 1997 [2] for using a special discrete wavelet transform (DWT) to compute the discrete Fourier transform (DFT). In the first step of the algorithm, the input sequence is decomposed in a similar manner to the SB-DFT into two sequences using wavelet decomposition with Haar filters. The second step is to perform DFTs on both bands to obtain the full-band DFT or to obtain a fast approximate DFT by implementing pruning at both input and output sides. In this paper, the wavelet-based DFT (W-DFT) with Haar filters is interpreted as SB-DFT with Hadamard filters. The only difference is in a constant factor in the combination network. This result is very important to complete the analysis of the W-DFT, since all the results concerning the accuracy and approximation errors in the SB-DFT are applicable. An application example in spectral analysis is given for both SB-DFT and W-DFT (with different filters). The adaptive capability of the SB-DFT is included in the W-DFT algorithm to select the band of most energy as the band to be computed. Finally, the W-DFT is extended to the two-dimensional case. An application in image transformation is given using two different types of wavelet filters.
Abstract: Wavelet transforms is a very powerful tools for image compression. One of its advantage is the provision of both spatial and frequency localization of image energy. However, wavelet transform coefficients are defined by both a magnitude and sign. While algorithms exist for efficiently coding the magnitude of the transform coefficients, they are not efficient for the coding of their sign. It is generally assumed that there is no compression gain to be obtained from the coding of the sign. Only recently have some authors begun to investigate the sign of wavelet coefficients in image coding. Some authors have assumed that the sign information bit of wavelet coefficients may be encoded with the estimated probability of 0.5; the same assumption concerns the refinement information bit. In this paper, we propose a new method for Separate Sign Coding (SSC) of wavelet image coefficients. The sign and the magnitude of wavelet image coefficients are examined to obtain their online probabilities. We use the scalar quantization in which the information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also examined. We show that the sign information and the refinement information may be encoded by the probability of approximately 0.5 only after about five bit planes. Two maps are separately entropy encoded: the sign map and the magnitude map. The refinement information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also entropy encoded. An algorithm is developed and simulations are performed on three standard images in grey scale: Lena, Barbara and Cameraman. Five scales are performed using the biorthogonal wavelet transform 9/7 filter bank. The obtained results are compared to JPEG2000 standard in terms of peak signal to noise ration (PSNR) for the three images and in terms of subjective quality (visual quality). It is shown that the proposed method outperforms the JPEG2000. The proposed method is also compared to other codec in the literature. It is shown that the proposed method is very successful and shows its performance in term of PSNR.
Abstract: In this paper, an efficient local appearance feature
extraction method based the multi-resolution Curvelet transform is
proposed in order to further enhance the performance of the well
known Linear Discriminant Analysis(LDA) method when applied
to face recognition. Each face is described by a subset of band
filtered images containing block-based Curvelet coefficients. These
coefficients characterize the face texture and a set of simple statistical
measures allows us to form compact and meaningful feature vectors.
The proposed method is compared with some related feature extraction
methods such as Principal component analysis (PCA), as well
as Linear Discriminant Analysis LDA, and independent component
Analysis (ICA). Two different muti-resolution transforms, Wavelet
(DWT) and Contourlet, were also compared against the Block Based
Curvelet-LDA algorithm. Experimental results on ORL, YALE and
FERET face databases convince us that the proposed method provides
a better representation of the class information and obtains much
higher recognition accuracies.