A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences

Abstract. The NLMeans filter, originally proposed by Buades et al., is a very popular filter for the removal of white Gaussian noise, due to its simplicity and excellent performance. The strength of this filter lies in exploiting the repetitive character of structures in images. However, to fully take advantage of the repetitivity a computationally extensive search for similar candidate blocks is indispensable. In previous work, we presented a number of algorithmic acceleration techniques for the NLMeans filter for still grayscale images. In this paper, we go one step further and incorporate both temporal information and color information into the NLMeans algorithm, in order to restore video sequences. Starting from our algorithmic acceleration techniques, we investigate how the NLMeans algorithm can be easily mapped onto recent parallel computing architectures. In particular, we consider the graphical processing unit (GPU), which is available on most recent computers. Our developments lead to a high-quality denoising filter that can process DVD-resolution video sequences in real-time on a mid-range GPU

The Impact of Class Clustering on a System with a Global FCFS Service Discipline

This paper considers a continuous-time queueing model with two types (classes) of customers each having their own dedicated server with exponential service times. The system adopts a global FCFS service discipline, i.e., all arriving customers are accommodated in one single FCFS queue, regardless of their types. Class clustering, i.e., the fact that customers of any given type may (or may not) have a tendency to arrive back-to-back, is a concept that we believe is often neglected in literature. As it is clear that customers of different types hinder each other more as they tend to arrive in the system more clustered according to class, the major aim of this paper is to estimate the impact of the degree of class clustering on the system performance. In this paper both classes of customers have an own cluster parameter. The motivation of our work are systems where this kind of blocking is encountered, such as input-queueing network switches, security checkpoints or road splits