Hanqiang Cao

FRACTAL CHARACTERS OF PORE MICROSTRUCTURES OF TEXTILE FABRICS

It is found that the pore microstructures of textile fabrics, widely used in the manufacture of fiber-reinforced composites, exhibit the fractal characters. The fractal behaviors are described by the proposed analytical method and measured by the box-counting method for the three different types of textile fabrics: plain woven, four-harness, bidirectional-stitched fiberglass mats. The pore area fractal dimension is derived analytically and found to be the function of the porosity and architectural parameters of fabrics. The results indicate that the fractal characters are isotropic although the fabrics are rothotropic in structures. The theoretical predictions by the proposed analytical model are in good agreement with those from the box-counting method, and this verifies the proposed fractal dimension model. The present fractal analysis may have the potential and significance on fractal analysis of transport properties (such as the permeability, dispersion, thermal and mechanical properties) in porous media.

Multiplicative Spread Spectrum Watermarks Detection Performance Analysis in Curvelet Domain

A multiplicative spread spectrum watermarking technique in curvelet domain is presented. Watermarked curvelet coefficients are modeled using generalized Gaussian distribution, Laplacian distribution and Cauchy model. Watermarking detectors are designed employing locally most powerful (LMP) approach. The detection performances of three detectors are analyzed. Experimental results show LMP detector based Cauchy model is superior to the detectors based generalized Gaussian and Laplacian distribution Keywords-curvelet transform; watermarking; locally most powerful; statistical detection

Multisensor image fusion using fast discrete curvelet transform

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.

Advances on image interpolation based on ant colony algorithm

This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses local weighting scheme. The strength of the proposed global weighting of AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper.

Optimization of Bilinear Interpolation Based on Ant Colony Algorithm

This paper proposes a novel method optimizing the bilinear interpolation using Ant Colony Algorithm (ACA). The isotropous-assignment of the old-pixels’ average-value to an unknown point (or pixel), in bilinear interpolation, is responsible for many artifacts which corrupt the resulting image features, edges in particular. To correct, we apply the ant colony algorithm to find a reasonable value from all the directions in the neighborhoods of the unknown point. The experimental results show better immanent image features than bilinear interpolation.

Computer-generated digital holograms based on IFS and application

With the exceptional development and popularization of laser holography, the application of the holographic anti- counterfeiting identifiers is enlarging gradually. How to improve the quality of holograms for laser holography industries becomes very important. In recent years computer- generated holograms have been investigated intensively because of their wide application range and their advantages in term of flexibility, accuracy, light weight and cost. A method based on Iterated Function System for digital hologram synthesis is proposed in this paper. The method can generate the view angle combining digital holograms by resolving the affine transforms of IFS into many affine transforms of sub- images. The reconstructed sequence of images with multi- channel can be viewed in different angles and look like a kinetic-object. Thus our method provides a new way to laser holography anti-counterfeiting. The generated fractal kinetic holograms have been used in many security holograms.

Image interpolation based on the pixel value corresponding to the smallest absolute difference

This paper proposes a novel algorithm for image interpolation. The motivation is based on relentless image interpolation artefacts and computational complexity. The solution proposed is founded on reprocessing one of the four pixels surrounding the unknown location and calculating the mean between that pixel and the value introduced by the bilinear interpolation. Subsequently, the mean is multiplied by the control factor k whose value is selected according to experimental analysis. Experimental results are further presented to show the effectiveness and performance of the proposed algorithm.

Complicated self-similarity of terrain surface

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.

No-reference image quality assessment using shearlet transform

Image and video quality measurements are crucial for many applications, such as acquisition, compression, transmission, enhancement, and reproduction. Nowadays, no-reference (NR) image quality assessment (IQA) methods have been drawn extensive attention because it does not need any information of reference images. However, most proposed NR IQA methods are designed only for one or a set of predefined specific distortion types, which are unlikely to generalize for evaluating images distorted with other types of distortions. In order to estimate a wide range of image distortions, in this paper, a novel NR IQA method is proposed which is based on shearlet transform, a new multiscale directional transform with a strong ability to localize distributed discontinuities. The distorted image leads to significant variation in the distributed discontinuities in all directions. Thus, the statistical property of the distorted image is significantly different from that of natural images in shearlet domain. A new model is also proposed to measure this difference. Numerical experiments demonstrate that this new NR IQA method is consistent with subjective assessment, very effective for many well-known types of image distortions and superior to some existing prominent methods.

An edge detection method for strong noisy image using shearlets

Numerous edge detection methods have been proposed to detect image edges. However, these methods are not very effective in detecting edges in strong noisy images. Recent years, multiscale analysis has been introduced to the realm of image processing. As the third generation wavelet, shearlets have their own superiority. Anisotropic dilation operator and shear operator are introduced to overcome the shortcomings of traditional wavelets. Because of their sensitivity to directions, shearlets are apt to do the job of edge detection. Based on shearlets, in this paper, a new edge detection method is proposed. The main idea about this new method is combining the shearlet denoising method with the edge detecting method based on shearlets. Analyzing results show that edges are characterized as zerocrossing points in shearlet domain and can be extracted from shearlet transform coefficients by detecting zero crossing points and using boundary tracking method. Many experiments are conducted to test this novel approach and we also compare Sobel, Log and Canny operators with this new method. Experiments demonstrate that when an image existing high deviation Gaussian noise, this method are much better than ordinary edge detection operators in time domain.