2,697 research outputs found

    Multi-focal image fusion technique using convolutional neural network

    Full text link
    In this paper, a solution to the problems faced by different images such as multifocal and medical images is found through a simulation process using brain magnetic resonance imaging (MRI) to make a fuse based on previously approved fusion techniques such as convolutional neural networks (CNN). An algorithm is developed with the introduction of the Euclidean distance algorithm as part of the processes to make the implementation faster and more efficient than the traditional CNN. The objective fusion metrics that are commonly used are implementing to make a quantitative evaluation. The proposed system consists of three main phases which are, pre-processing phase, features extraction phase, and classification phase. The preprocessing phase is used to enhance the images by using the techniques of digital image processing. Feature extraction phase is used to get features from medical images based on the concept of Histogram of Orientation Gradient (HOG) technique feature that applied to the medical image after conversion using mean filter, adaptive filter, Discrete Wavelet Transform (DWT), k-means clustering Singular Value Decomposition (k-SVD)

    Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization

    Full text link
    Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of nonnegative data. Recently, Graph regularized NMF (GrNMF) is proposed to find a compact representation,which uncovers the hidden semantics and simultaneously respects the intrinsic geometric structure. In GNMF, an affinity graph is constructed from the original data space to encode the geometrical information. In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space. The GrNMF is improved by utilizing the graph refined by the kernel learning, and then a novel kernel learning method is introduced under the GrNMF framework. Our approach shows encouraging results of the proposed algorithm in comparison to the state-of-the-art clustering algorithms like NMF, GrNMF, SVD etc.Comment: This paper has been withdrawn by the author due to the terrible writin

    Multi-source and Multi-feature Image Information Fusion Based on Compressive Sensing

    Get PDF
    Image fusion is a comprehensive information processing technique and its purpose is to enhance the reliability of the image via the processing of the redundant data among multiple images, improve the image definition and information content through fusion of the complementary information of multiple images so as to obtain the information of the objective or the scene in a more accurate, reliable and comprehensive manner. This paper uses the sparse representation method of compressive sensing theory, proposes a multi-source and multi-feature image information fusion method based on compressive sensing in accordance with the features of image fusion, performs sparsification processing on the source image with K-SVD algorithm and OMP algorithm to transfer from spatial domain to frequency domain and decomposes into low-frequency part and high-frequency park. Then it fuses with different fusion rules and the experimental results prove that the method of this paper is better than the traditional methods and it can obtain better fusion effects

    Fuzzy Logic and Singular Value Decomposition based Through Wall Image Enhancement

    Get PDF
    Singular value decomposition based through wall image enhancement is proposed which is capable of discriminating target, noise and clutter signals. The overlapping boundaries of clutter, noise and target signals are separated using fuzzy logic. Fuzzy inference engine is used to assign weights to different spectral components. K-means clustering is used for suitable selection of fuzzy parameters. Proposed scheme significantly works well for extracting multiple targets in heavy cluttered through wall images. Simulation results are compared on the basis of mean square error, peak signal to noise ratio and visual inspection
    corecore