Image denoising is a challenging issue found in diverse image processing and computer vision problems. There are various existing methods investigated to denoising image. The essential characteristic of a successful model that denoising image is that it should eliminate noise as far as possible and edges preserving and necessary image information by improving visual quality. This paper presents a review of some significant work in the field of image denoising based on that the denoising methods can be roughly classified as spatial domain methods, transform domain methods, or can mix both to get the advantages of them. This work tried to focus on this mixing between using wavelet transform and the filters in spatial domain to show spatial domain. There have been numerous published algorithms, and each approach has its assumptions, advantages, and limitations depending on the various merits and noise. An analyzing study has been performed comparative in their methods to achieve the denoising algorithms, filtering approach and wavelet-based approach. Standard measurement parameters have been used to compute results in some studies to evaluate techniques while other methods applied new measurement parameters to evaluate the denoising techniques.
In a recent past, face recognition was one of the most popular methods and successful application of image processing field which is widely used in security and biometric applications. The innovation of new approaches to face identification technologies is continuously subject to building much strong face recognition algorithms. Face recognition in real-time applications has been fast-growing challenging and interesting. The human face identification process is not trivial task especially different face lighting and poses are captured to be matched. In this study, the proposed method is tested using a benchmark ORL database that contains 400 images of 40 persons as the variant posse, lighting, etc. Discrete avelet Transform technique is applied on the ORL database to enhance the accuracy and the recognition rate. The best recognition rate result obtained is 99.25%, when tested using 9 training images and 1 testing image with cosine distance measurement. The recognition rate Increased when applying 2-level of DWT with the bior5.5 filter on training image database and the test image. For feature extraction and dimension reduction, PCA is used. Euclidean distance, Manhattan distance, and Cosine distance are Distance measures used for the matching process.
Face recognition has become an attractive field in computer based application development in the last few decades. That is because of the wide range of areas they used in. And because of the wide variations of faces, face recognition from the database images, real data, capture images and sensor images is challenging problem and limitation. Image processing, pattern recognition and computer vision are relevant subjects to face recognition field. The innovation of new approaches of face authentication technologies is continuous subject to build much strong face recognition algorithms. In this work, to identify a face, there are three major strategies for feature extractions are discussed. Appearance-based and Modelbased methods and hybrid techniques as feature extractions are discussed. Also, review of major person recognition research the characteristics of good face authentication applications, Classification, Distance measurements and face databases are discussed while the final suggested methods are presented. This research has six sections organized as follow: Section one is the introduction. Section two is dedicated to applications related to face recognition. In Section three, face recognition techniques are presented by details. Then, classification types are illustrated in Section four. In section five, standard face databases are presented. Finally, in Section six, the conclusion is presented followed by the list of references.
In the field of networking, software-defined networking (SDN) has obtained a lot of concentration from both academic and industry, and it aims to provide a flexible and programmable level of control, beside obtain efficient control and management of network systems. For such reasons, the software-defined networks (SDN) can be deemed as an essential task to accomplish these requirements. In the datacenters and networks, the SDN is used to allow the administrators of the networks to start programming, controlling, changing, and managing dynamically the network behavior with open interfaces and a reflection of lower-level functionality because the need for SDN-like switching technology has become evident for many users of network equipment, especially in large data centers. There are many algorithms and applications that have been considered in SDN such as (FP-MA), EON, (EQUAL-APP) (VONCR-APP), and (T-SDN) as use cases for approval purposes because the SDN provides several focal points to the power, operation, and administration of extensive range networks. This paper aims to review Optical Network using SDN, where many types of research papers are present techniques to improve near-optimal traffic engineering and management; measurement and monitoring of the significant parameters of the optical networks and manage the cross-layer issues such as debugging and testing.
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