Recently, stable processes have turned out to be good models for many impulsive signals and noises. The speckle noise
in underwater, SAR and the cosmic background images has been proved to have heavy tails distributions and Long Rang
Dependent (LRD) structures. In this paper, the Fractional Levy Stable Motion (FLSM) is introduced to model such
speckle phenomenon. The synthesis approaches employing Random Midpoint Displacement (RMD) and FFT technology
are presented to generate such speckle image respectively. Then, we introduce Wavelet Analysis (WA) method to
estimate the LRD exponent H and propose two new technologies in estimation H parameter by Fractional Low Order
Moment (FLOM) and Fractional Spectrum (FS) respectively.
Fractal-based analysis provides an excellent explanation of the ruggedness of natural surface. Fractal-based description of
image texture has been used effectively in characterization of natural scene. However, a real surface is not always so
perfect that keep invariable self-similarity in whole scale space and it seems to be multi-scale. The present work focused on
the modeling of underwater image. We analyzed the increment distribution and the self-similarity of several typical
objects, and the results show that the traditional FBM model is not suitable for modeling such objects. And then one kind of
self-similar stable process, Fractional Lévy Stable Motion (FLSM) is discussed. Based on FLSM, we proposed a new
model, Multi-scale Fractional Lévy Stable Motion (MFLSM) in which the self-similarity parameter H (Δs) is a variable
with respect to measure scale s. Furthermore such 2-D model is applied to model the scattering image. The simulation
result shows that MFLSM represents the multi-scale self-similarity and the speckle of underwater optical imaging.
In this paper, we analyzed the increment distribution and the self-similarity behavior of texture images of three kinds of
particular underwater objects during the mineral hunting process. The experimental data has shown that the <i>H</i> exponent
of real underwater natural texture is not a constant over all scale range, but a variable with respect to the measure scale or
time index. In order to investigate the multi-scale self-similarity behavior of the objects, we had extended the traditional
FBM so that the self-similarity parameter <i>H</i> is taken as a variable <i>H</i>(<i>s</i>) with respect to measure scale <i>s</i>. The class-separability
of self-similarity feature is measured, and the feature selection criterion is given. Pattern classification
simulation experimental results have shown the effectiveness of the selected feature set combining the self-similarity
parameter <i>H</i><sub>Δ</sub>(3), the variance <i>D</i>(<i>H</i><sub>Δ</sub>) and the increment variance <i>AD</i>. The correct ratio is up to 96% on average, which
can be used in automatic detection and recognition for AUVs to complete their tasks.
Many advance image processing, like segmentation and recognition, are based on contour extraction which usually lack of ability to allocate edge precisely in the image of heavy noise with low computation burden. For such problem, in this paper, we proposed a new approach of edge detection based on pyramid-structure wavelet transform. In order to suppress noise and keep good continuity of edge, the proposed edge representation considered both inter-correlations across the multi-scales and intra-correlations within the single-scale. The former one is described by point-wise singularity. The later one is described by the magnitude and ratio of wavelet coefficients in different sub-bands. Based on such edge modeling, the edge point allocation is then complemented in wavelet domain by synthesizing the edge information in multi-scales. The experimental results shows that our approaches achieve the pixel-level edge detection with strong resistant against noise due to scattering in water.
The existing methods for texture modeling include co-occurrence statistics, filter banks and random fields. However most of these methods lack of capability to characterize the different scale of texture effectively. In this paper, we propose a texture representation which combines local scale feature, amplitude and phase of wavelet modules in multi-scales. The self-similarity of texture is not globally uniform and could be measured in both correlations across the multi-scale and statistical feature within a single-scale. In our approach, the local scale feature is represented by optimal scale obtained through the evolution of wavelet modulus across multi-scales. Then, for all the blocks of the same optimal scale, the statistical measurement of amplitude is extracted to represent the energy within the corresponding frequency
band; the statistical measurement of the phase of modulus is extracted to represent the texture's orientation. Our experiment indicates that, in the proposed texture representation the separability of different texture patterns is larger than the one of the traditional features.
In order to improve the quality of image with super-resolution reconstruction, a method based on motion estimation error and edge constraint was proposed. Under the condition of data consistency and amplitude restriction, the motion estimation error was analyzed, with its variance being calculated; meanwhile, in order to suppress the ringing artifacts, edge constraint was adopted and a method based clustering for judging the edge's direction was proposed. The experimental results show that the performance of the this algorithm is better than the traditional linear interpolation and method without considering motion estimation error both in vision effect and peak signal to noise ratio.
Self-similarity features of natural surface play a key role in region segmentation and recognition. Due to long period of
natural evolution, real terrain surface is composed of many
self-similar structures. Consequently, the Self-similarity is
not always so perfect that remains invariable in whole scale space and the traditional single self-similarity parameter can
not represent such abundant self-similarity. In this view, the
self-similarity is not a constant parameter over all scales, but
multi-scale parameters. In order to describe such multi-scale
self-similarities of real surface, firstly we adopt the
Fractional Brownian Motion (FBM) model to estimate the
self-similarity curve of terrain surface. Then the curve is
divided into several linear regions to represent relevant
self-similarities. Based on such regions, we introduce a parameter
called Self-similar Degree (<i>SSD</i>) in the similitude of information entropy. Moreover, the small value of <i>SSD</i> indicates the
more consistent self-similarity. We adopt fifty samples of terrain images and evaluate SSD that represents the multi-scale
self-similarity features for each sample. The samples are clustered by unsupervised fuzzy c mean clustering into various
classes according to <i>SSD</i> and traditional monotone Hurst feature respectively. The measurement for separability of
features shows that the new parameter <i>SSD</i> is an effective feature for terrain classification. Therefore the similarity
feature set that is made up of the monotone Hurst parameter and SSD provides more information than traditional
monotone feature. Consequently, the performance of terrain classification is improved.
Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. But in the traditional methods of watermarking images, the information of original image will be distorted more or less. Facing this problem, a new watermarking approach, zero-watermarking technique, is proposed. The zero-watermarking approach changes the traditional doings that watermarking is embedded into images, and makes the watermarked image distortion-free. Zero-watermarking can successfully solve the conflict between invisibility and robustness. In this paper, a digital image zero-watermarking method based on discrete wavelet transform and chaotic modulation is proposed.
The zero-watermarking algorithm based on DWT and chaos modulation consists of watermark embedding and detecting processes.
The watermark embedding process is as follow:
First, the original image is decomposed to three-level in wavelet domain. Second, some low frequency wavelet coefficients of original image are selected. The selection of the wavelet coefficients is random by chaotic modulation. Third, the character of coefficients selected is used to construct the character watermark. For each coefficient, in comparison with the adjacent coefficient, we can get the character watermark.
The watermark extracting process is invert process. The location of the coefficients being extracted is also determined by chaotic sequence.
The experimental results show that the watermarking method is invisible and robust against some image processing such as median filtering, JPEG compression, additive Gaussian noise, cropping and rotation attacks and so on. If the initial value of chaos is unknown, the character watermarking can't be extracted correctly.
Recently the protection of digital information has received significant attention and lots of techniques have been proposed. Digital watermarking is an efficacious technique to protect the copyright and ownership of digital information. Since 90' various implementation approaches about digital watermarking have been presented. In this paper, an adaptive blind watermarking algorithm for still images is proposed based on discrete wavelet transform. The wavelet theory mainly used in the multi-resolution analysis and recently has been applied to watermarking technique. In order to be more robust, the embedding strength is decided based on the background luminance and the texture mask characters of HVS, which is adaptive to the carrier image. The experimental results show that the watermarked image has a good quality of image, watermark is imperceptibility, the algorithm is robust against some image processing such as median filtering, JPEG lossy compression, additive Gaussian noise and cropping attacks and so on. Hence our method can be used to protect effectively property right of digital images.
Fractal describes the self-similar phenomenon of signal and self-similarity is the most important character of fractal. Pentland provides an excellent explanation of the ruggedness of natural surface. Fractal-based description of image texture has been used effectively in characterization and segmentation of natural scene. A real surface is self-similar over some range of scales, rather than over all scales. That imply self-similarity of a terrain surface is not always so perfect that keep invariable in whole scale space. To describe such self-similarity distribution, a self-similarity curve could be plotted and was divided into several linear regions. We present a new parameter called Self-similarity Degree (SD) in the similitude of information entropy to denote such self-similarity distribution. In addition, one general characterization of self-similarities is result of physical processes. Terrain surface are created by the interactional inogenic and exogenic processes. Hereby, we introduce self-similarity analysis and multifractal singularity spectrum to describe such complex physical field. By the self-similarity analysis and singularity spectrum, the different self-similar structures and the interaction of processes in terrain surface were depicted. Our studies have shown that self-similarity is a relative notion and natural scenes own abundant self-similar structures. Moreover, noises always destroy the self-similarity of original natural surface and change the singularity distribution of original surface.
Digital watermarking has been recently proposed as the mean for property right protection of digital products. In this paper we analyze the self-similarities of wavelet transform and present a new approach to embed a digital watermark into an image based on the qualified significant wavelet trees (QSWT) of discrete wavelet transform of the image for the purpose of protecting the copyright of the image. Our studies have shown that the watermarked image has a good quality of image, and such a watermark is difficult to detect and unchangeable without the appropriate user cryptogram.
Traditional algorithms of 3D surface reconstruction include iteration and linear interpolation and surface fitting technique etc. These methods are mainly suitable for regular shapes and smooth surfaces. For scenes with affluent texture, these methods can't preserve their statistical characteristics. A new method combining wavelet decomposition with fractal interpolation for 3D surface reconstruction is proposed in this paper. For 3D surface reconstruction purposes, the wavelet decomposition extracts strong image self-similar characteristics that can be utilized to three dimension surface reconstruction. With a fractal interpolation, we can generate the practical simulation of three dimension visual surfaces. Our results indicate a good approximation of realistic looking mountainous terrain.