Improved Non-Local Means Algorithm Based on Dimensionality Reduction
نویسندگان
چکیده
Non-Local Means is an image denoising algorithm based on patch similarity. It compares a reference patch with the neighboring patches to find similar patches. Such similar patches participate in the weighted averaging process. Most of the computational time for Non-Local Means is consumed to measure patch similarity. In this thesis, we have proposed an improvement where the image patches are projected into a global feature space. Then we have performed a statistical t-test to reduce the dimensionality of this feature space. Denoising is achieved based on this reduced feature space and the proposed modification exploits an improvement in terms of denoising performance and computational time. without whom my success would be impossible. iv Acknowledgments
منابع مشابه
Diagnosis of Diabetes Using an Intelligent Approach Based on Bi-Level Dimensionality Reduction and Classification Algorithms
Objective: Diabetes is one of the most common metabolic diseases. Earlier diagnosis of diabetes and treatment of hyperglycemia and related metabolic abnormalities is of vital importance. Diagnosis of diabetes via proper interpretation of the diabetes data is an important classification problem. Classification systems help the clinicians to predict the risk factors that cause the diabetes or pre...
متن کاملFacial expression recognition based on Local Binary Patterns
Classical LBP such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. In this paper, we introduce an improved LBP algorithm to solve these problems that utilizes Fast PCA algorithm for reduction of vector dimensions of extracted features. In other words, proffer method (Fast PCA+LBP) is an improved LBP algorithm that is extracted ...
متن کاملA Dynamic K Value Method of Massive Data Reduction Based on Improved LLE Algorithm
For more features massive data sets efficiently reduction problems, to enhance the data uniformity of the near target instance, reduce the errors of represented neighbors, improve the effect of dimension reduction, proposed a dynamic K value method of massive data reduction based on improved LLE algorithm. Utilized the search algorithms based center point, searched an example neighborly, to ens...
متن کاملImproved COA with Chaotic Initialization and Intelligent Migration for Data Clustering
A well-known clustering algorithm is K-means. This algorithm, besides advantages such as high speed and ease of employment, suffers from the problem of local optima. In order to overcome this problem, a lot of studies have been done in clustering. This paper presents a hybrid Extended Cuckoo Optimization Algorithm (ECOA) and K-means (K), which is called ECOA-K. The COA algorithm has advantages ...
متن کاملSelf-organizing Piecewise Aggregate Approximation Algorithm for Intelligent Detection and Diagnosis of Heart Conditions
Electrocardiogram (ECG) signal classification is a recognized method for automated detection and diagnosis of heart abnormalities. This is typically achieved through dimensionality reduction techniques and feature extraction followed by signal classification using various machine learning algorithms. Although some algorithms can yield accurate results, they can be computationally demanding mean...
متن کامل