An advanced non-linear cascading filter algorithm for the removal of high density salt and pepper noise from the digital images is proposed. The proposed method consists of two stages. The first stage Decision base Median Filter (DMF) acts as the preliminary noise removal algorithm. The second stage is either Modified Decision Base Partial Trimmed Global Mean Filter (MDBPTGMF) or Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) which is used to remove the remaining noise and enhance the image quality. The DMF algorithm performs well at low noise density but it fails to remove the noise at medium and high level. The MDBPTGMF and MDUTMF have excellent performance at low, medium and high noise density but these reduce the image quality and blur the image at high noise level. So the basic idea behind this paper is to combine the advantages of the filters used in both the stages to remove the Salt and Pepper noise and enhance the image quality at all the noise density level. The proposed method is tested against different gray scale images and it gives better Mean Absolute Error (MAE)

Cloud computing is rapidly growing for its on-demand services over the Internet. The customers can use these services by placing the requirements in the form of leases. In IaaS cloud, the customer submits the leases in one of the form, namely advance reservation (AR) and best effort (BE). The AR lease has higher priority over the BE lease. Hence, it can preempt the BE lease. It results in starvation among the BE leases and is unfair to the BE leases. In this chapter, the authors present fairness-aware task allocation (FATA) algorithm for heterogeneous multi-cloud systems, which aims to provide fairness among AR and BE leases. We have performed rigorous experiments on some benchmark and synthetic datasets. The performance is measured in terms of two metrics, namely makespan and average cloud utilization. The experimental result shows the superiority of the proposed algorithm over the existing algorithm.

A modified spatially adaptive denoising algorithm for a single image corrupted by Gaussian noise is proposed in this paper. The proposed algorithm use local statistics of a selected window i.e. by defining local weighted mean, local weighted activity and local maximum. These local statistics are used to detect the noise in the image then a modified Gaussian filter is used for noise suppression. This algorithm is tested against different images and the experimental result shows its result is better than different existing methods like Pixel Wise Median Absolute Difference (PWMAD), Rank Order Criteria (ROC), Switching-based Adaptive Weighted Mean (SAWM) and Spatially Adaptive Denoising Algorithm (SADA).