Analysis of Multi-Scale Fractal Dimension for Image Interpolation

This article presents a novel image interpolation based on rational fractal fimction. The rational function has a simple and explicit expression. At the same time, the fi＇actal interpolation surface can be defined by proper parameters. In this paper, we used the method of ＇covering blanket＇ combined with multi-scale analysis; the threshold is selected based on the multi-scale analysis. Selecting different parameters in the rational function model, the texture regions and smooth regions are interpolated by rational fractal interpolation and rational interpolation respectively. Experimental results on benchmark test images demonstrate that the proposed method achieves very competitive performance compared with the state-of-the-art interpolation algorithms, especially in image details and texture features.

Saliency detection based on superpixels clustering and stereo disparity

Reliable saliency detection can be used to quickly and effectively locate objects in images. In this paper, a novel algorithm for saliency detection based on superpixels clustering and stereo disparity （SDC） is proposed. Firstly, we use an improved superpixels clustering method to decompose the given image. Then, the disparity of each superpixel is computed by a modified stereo correspondence algorithm. Finally, a new measure which combines stereo disparity with color contrast and spatial coherence is defined to evaluate the saliency of each superpixel. From the experiments we can see that regions with high disparity can get higher saliency value, and the saliency maps have the same resolution with the source images, objects in the map have clear boundaries. Due to the use of superpixel and stereo disparity information, the proposed method is computationally efficient and outperforms some state-of-the-art color- based saliency detection methods.

Tire Defect Detection on Impurities

According to X-ray image texture tire characteristics and abnormal distribution of the gray area, this paper studies a real-time defect detection method about the sidewall of tire. On the basis of the texture distribution characteristics of X-ray tire, we extract the cord information of the tire image by maximizing the distance of the categories after the gray value correction, then we use the morphology method and statistics method to process the image so that we can get the location and the size of impurities. We can classify the tires into different grades by the information. Compared with other methods, our method has the advantage that meets the needs of tire industry.

Adaptive image decomposition method based on credible data fitting with local total variation

In this paper we present a novel image decomposition method via credible data fitting with local total variation filter. The oscillation rate is used to measure the image complexity and characteristics. The filter parameter can be determined by a fitting curve which is reconstructed by oscillation rate. In addition, the approximate Gaussian algorithm and integral image are used to reduce the algorithm computation and the sensitivity of the filter window selection. Experiments show the new method is better than the exist- ing methods.

Constructing C^1 Continuous Surface on Irregular Quad Meshes

A new method is proposed for surface construction on irregular quad meshes as extensions to uniform B-spline surfaces. Given a number of control points, which form a regular or irregular quad mesh, a weight function is constructed for each control point. The weight function is defined on a local domain and is C1 continuous. Then the whole surface is constructed by the weighted combination of all the control points. The property of the new method is that the surface is defined by piecewise Cl bi-cubic rational parametric polynomial with each quad face. It is an extension to uniform B-spline surfaces in the sense that its definition is an analogy of the B-spline surface, and it produces a uniform bi-cubic B-spline surface if the control mesh is a regular quad mesh. Examples produced by the new method are also included.

A New Medical Image Enhancement Based on Human Visual Characteristics

Study of image enhancement shows that the quality of image heavily relies on human visual system. In this paper, we apply this fact effectively to design a new image enhancement method for medical images that improves the detail regions. First, the eye region of interest （ROI） is segmented; then the Un-sharp Masking （USM） is used to enhance the detail regions. Experiments show that the proposed method can effectively improve the accuracy of medical image enhancement and has a significant effect.

Polling Algorithm Based on Traffic Burst Characteristics of Broadband Wireless Access Systems

In this paper,we propose a novel polling algorithm to decrease the number of idle slots and collission slots during the polling cycle by evaluating the bandwidth request of the subscribe stations(SSs) in the next polling cycle for broadband wireless access(BWA) systems.We firstly analyze the feature of silent time intervals and deduce the Hurst index which symbolizes the degree of self-similarity and long-relative nature.Then we represent the inactive OFF time intervals by the Pareto model and estimate the shape parameterαby the group measured data.Finally we can evaluate the transmission probability of a silent SS before the next polling cycle. By this algorithm,we can find the optimal transmission opportunities for the base station(BS) to achieve the least collision or void timeslots in order to achieve the largest bandwidth efficiency.The theoretical analysis and simulation results show that this polling algorithm can improve the performance for BWA system.

Enlarging Image by Constrained Least Square Approach with Shape Preserving

From a visual point of view, the shape of an image is mainly determined by the edges. Conventional polynomial interpolation of image enlarging methods would produce blurred edges, while edge-directed interpolation based methods would cause distortion in the non-edge areas. A new method for image enlarging is presented. The image is enlarged in two steps. In the first step, a fitting surface is constructed to interpolate the image data. To remove the zigzagging artifact for each pixel, a fitting patch is constructed using edge information as constraints. The combination of all the patches forms the fitting surface which has the shape suggested by image data. Each point on the fitting surface can be regarded as a sampling point taken from a unit square domain, which means that when the fitting surface is used to enlarge the image, each sampling domain of the enlarged pixels is also a unit square, causing the enlarged image to lose some details. To make the enlarged image keep the details as many as possible, the sampling domain of the enlarged pixels should be less than a unit square. Then, in the second step, using the points taken from the fitting surface, new pixels are computed using constrained optimization technique to form the enlarged image, and the size of the sampling domain of the enlarged pixels is inversely proportional to the size of the enlarged image. The image enlarged by the new method has a quadratic polynomial precision. Comparison results show that the new method produces resized image with better quality.

A nonlocal gradient concentration method for image smoothing

It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradientsparsity constraint, allowing details throughout the whole image to be removed automatically in a datadriven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal,and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications,including edge manipulation, image abstraction,detail magnification, and image resizing, show the applicability of the new method.

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.