Image enhancement is considered as one of the most important techniques in image research. The main aim of image enhancement is to enhance the quality and visual appearance of an image, or to provide a better transform representation for future automated image processing. Many images like medical images, satellite, aerial images and also real life photographs suffer from poor and bad contrast and noise. It is necessary to enhance the contrast and remove the noise to increase image quality. One of the most important stages in medical images detection and analysis is Image Enhancement Techniques. It improves the clarity of images for human viewing, removing blurring and noise, increasing contrast, and revealing details. These are examples of enhancement operations. The enhancement technique differs from one field to another depending on its objective. The existing techniques of image enhancement can be classified into two categories: Spatial Domain and Frequency Domain Enhancement. In this paper, we present an overview of Image Enhancement Processing Techniques in Spatial Domain. More specifically, we categorise processing methods based representative techniques of Image enhancement. Thus the contribution of this paper is to classify and review Image Enhancement Processing Techniques as well as various noises has been applied to the image. Also we applied various filters to identify which filter is efficient in removing particular noises. This is identified by comparing the values obtained in PSNR and MSE values. From this we can get an idea about which filters is best for removing which types of noises. It will be useful and easier to detect the filters for future research.

Image Enhancement Techniques: A Study

The cloud computing paradigm offers an innovative and promising vision concerning Information and Communications Technology. Actually, it provides the possibility of improving IT systems management and is changing the way in which hardware and software are designed and purchased. This paper introduces challenges in Global Software Development GSD and application of cloud computing platforms as a solution to some problems. Even though cloud computing provides compelling benefits and cost-effective options for GSD, new risks and difficulties must be taken into account. Thus, the paper presents a study about the risk issues involved in cloud computing. It highlights the different types of risks and how their existence can affect GSD. It also proposes a new risk management process model. The risk model employs new processes for risk analysis and assessment. Its aim is to analyse cloud risks quantitatively and, consequently, prioritise them according to their impact on different GSD objectives.

Cloud Computing for Global Software Development: Opportunities and Challenges

Cloud computing is emerged as new technology in organization and cooperates. To successfully provide cloud services and sharing of resources, the cloud must be tested before came into service. Cloud testing is a form of testing in which web applications uses cloud computing environment and infrastructure to simulate real world user traffic by using cloud technologies and solutions. The work on this paper is focused on various cloud computing testing techniques and cloud testing challenges faced by cloud service provider. There are three major techniques available for cloud testing which are functional, non-functional and ability cloud testing. These techniques are further subcategorizing as their testing areas. Later, in this paper the seven cloud testing challenge are discussed which are: service, security and layered testing challenge, lack of universal standard, guidance, knowledge and staff expertise, limited infrastructure, procuring cloud service on-demand basis. Keywords—Ability Testing; Cloud; Cloud Computing; Cloud Computing Challenges; Cloud Testing; Functional Testing; Non-Functional Testing. Abbreviations—Infrastructure as a Service (IaaS); Platform as a Service (PaaS); Software as a Service (SaaS); Testing as a Service (TaaS)

Testing Techniques and its Challenges in a Cloud Computing Environment

Cloud computing is Internet-based computing, whereby shared resources, software, and information are provided to computers and other devices on demand. Cloud computing is a natural evolution of the widespread adoption of virtualization, service-oriented architecture and utility computing. The computer applications nowadays are becoming more and more complex; there is an ever increasing demand for computing resources. As this demand has risen, the concepts of cloud computing and grid computing have found many followers with more and more companies looking  to outsource computing needs to a third party vendor. However, this growing dependence on third party providers is causing large corporations as well as research groups to look for alternative models that can support the increased demand for computing resources. The concerns of reliability, privacy, efficiency at the expense of resilience and environmental sustainability are at the forefront of such research. For application providers, cloud computing has the advantage that it reduces the administrative effort required to satisfy processing and storage requirements. However, to simplify the task of building scalable applications, some of the cloud computing platforms impose constraints on the application architecture, its implementation and tools that may be used in development. The changing scenario in education has given rise to need of technology in education. Information Technology has played a major part in improving the learning outcomes of individuals. Technologies like cloud computing has not only reduced the burden of economy as well as resources required. This paper has shown the different aspects of using cloud and non-cloud environment.

Development and Usage of Software as a Service for a Cloud and Non-Cloud Based Environment- An Empirical Study

Today Cloud computing is emerging as a new technology in corporate world and organization. Cloud testing is a form of software testing in which web applications use cloud computing environments (i.e. a "cloud") to handle with real-world user traffic by using various cloud technologies and solutions. Cloud computing leads an opportunity in offering testing as a service (TaaS) for SaaS and clouds. But at the same time, it causes new issues, challenges and needs in software testing, particular in the field of 1) testing clouds and 2) cloud-based applications. In this paper we discus some basic concepts of cloud testing, its type and major issues and challenges comes in this field. The paper also gives light on the benefits of cloud testing over conventional software testing.

Cloud Testing: A Review Article

Cloud computing is innovative model in which the information is permanently stored on the servers and manages the different resources for the requested users to provide on-demand services. In order to create the more useable and economic value based cloud computing, the principals, goals and structure of the cloud engineering is vital important. Cloud engineering is an interdisciplinary field of engineering that focus on cloud services. To understand the structure of cloud engineering, four components are given in this paper such as Base; Tools and Techniques; cloud development life cycle; and Management. Base component provides knowledge about various principles, methods and taxonomy of the cloud computing. Tools and Techniques component sets up and defines the various tools, techniques, utilities and libraries used for the implementation. The cloud development life cycle component is the iterated life cycle model for development and delivery of cloud. The Management of cloud computing solution is done from multiple prospective such as design and run time cloud management; configuration, operational, asset and risk management. These components give complete set of knowledge and are discussed one by one in the paper.

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The Structure of Cloud Engineering

Cloud computing is a model for on-demand and convenient network access to a shared pool of computing resources that can be easily used and provide services with the minimal management effort. Cloud engineering is a discipline for the systematic and scientifically development of the good and economic cloud that focus on providing cloud services to requested users. In order to create the economical and good quality cloud, the structure of cloud engineering is important. This structure consists of four components: Base, Cloud development life cycle (CDLC), Tools and Techniques, Management. Cloud Development life cycle (CDLC) is one of the important component of structure which has iterated life cycle model for development, deployment and delivery of cloud. CDLC has six phases which is flexible, simple in nature and having feedback to first phase. These phases are organized in a linear order and processed in an isolated manner. This paper discusses each phase of CDLC in detail one by one.

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Process Model for Cloud Service Engineering

Computer graphics is the branch of computer science which deals with the study of graphics and images. Advancement in the technology brings the development in the image processing techniques, which deals with the image acquisition, image enhancement, restoration etc. Different type of noise got added up to the image while image acquisition leading to the final corrupted image. The main objective of this paper is to remove noise from the gray scale images to better understand the contents of the original image by applying various filter algorithms to get the blurred free and noise free image. For this an algorithm is developed and presented in shape of flowchart. This paper compares the different filters used for the noise removal from an image. It studies various filters for noise removal and finds the opinion that which is best for every type of image. As every image processing algorithms works differently for different image, there are different methods to deal with different types of noise.

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Jain Prince

Papers

The Structure of Cloud Engineering

Process Model for Cloud Service Engineering

Analysis the Impact of Filters in Spatial Domain on Grayscale Image