A Robust and Fast Non-Local Means Algorithm for Image Denoising

In the paper, we propose a robust and fast image denoising method. The approach integrates both Non- Local means algorithm and Laplacian Pyramid. Given an image to be denoised, we first decompose it into Laplacian pyramid. Exploiting the redundancy property of Laplacian pyramid, we then perform non-local means on every level image of Laplacian pyramid. Essentially, we use the similarity of image features in Laplacian pyramid to act as weight to denoise image. Since the features extracted in Laplacian pyramid are localized in spatial position and scale, they are much more able to describe image, and computing the similarity between them is more reasonable and more robust. Also, based on the efficient Summed Square Image （SSI） scheme and Fast Fourier Transform （FFT）, we present an accelerating algorithm to break the bottleneck of non-local means algorithm - similarity computation of compare windows. After speedup, our algorithm is fifty times faster than original non-local means algorithm. Experiments demonstrated the effectiveness of our algorithm.

Pores-Preserving Face Cleaning Based on Improved Empirical Mode Decomposition

In this paper, we propose a novel method of cleaning up facial imperfections such as bumps and blemishes that may detract from a pleasing digital portrait. Contrasting with traditional methods which tend to blur facial details, our method fully retains fine scale skin textures （pores etc.） of the subject. Our key idea is to find a quantity, namely normalized local energy, to capture different characteristics of fine scale details and distractions, based on empirical mode decomposition, and then build a quantitative measurement of facial skin appearance which characterizes both imperfections and facial details in a unified framework. Finally, we use the quantitative measurement as a guide to enhance facial skin. We also introduce a few high-level, intuitive parameters for controlling the amount of enhancement. In addition, an adaptive local mean and neighborhood limited empirical mode decomposition algorithm is also developed to improve in two respects the performance of empirical mode decomposition. It can effectively avoid the gray spots effect commonly associated with traditional empirical mode decomposition when dealing with high-nonstationary images.

Mobile agent-enabled framework for structuring and building distributed systems on the internet

Mobile agent has shown its promise as a powerful means to complement and enhance existing technology in various application areas. In particular, existing work has demonstrated that MA can simplify the development and improve the performance of certain classes of distributed applications, especially for those running on a wide-area, heterogeneous, and dynamic networking environment like the Internet. In our previous work, we extended the application of MA to the design of distributed control functions, which require the maintenance of logical relationship among and/or coordination of processing entities in a distributed system. A novel framework is presented for structuring and building distributed systems, which use cooperating mobile agents as an aid to carry out coordination and cooperation tasks in distributed systems. The framework has been used for designing various distributed control functions such as load balancing and mutual ex- clusion in our previous work. In this paper, we use the framework to propose a novel approach to detecting deadlocks in distributed system by using mobile agents, which demonstrates the advantage of being adaptive and flexible of mobile agents. We first describe the MAEDD （Mobile Agent Enabled Deadlock Detection） scheme, in which mobile agents are dispatched to collect and analyze deadlock information distributed across the network sites and, based on the analysis, to detect and resolve deadlocks. Then the design of an adaptive hybrid algorithm derived from the framework is presented. The algorithm can dynamically adapt itself to the changes in system state by using different deadlock detection strategies. The performance of the proposed algorithm has been evaluated using simulations. The results show that the algorithm can outperform existing algorithms that use a fixed deadlock detection strategy.