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  • Open Access

    ARTICLE

    Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

    N. A. Lawrance*, T. S. Shiny Angel

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3439-3455, 2023, DOI:10.32604/csse.2023.030311 - 03 April 2023

    Abstract The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: More >

  • Open Access

    ARTICLE

    An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

    Walid El-Shafai1,2, Noha A. El-Hag3, Ahmed Sedik4, Ghada Elbanby5, Fathi E. Abd El-Samie1, Naglaa F. Soliman6, Hussah Nasser AlEisa7,*, Mohammed E. Abdel Samea8

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2905-2925, 2023, DOI:10.32604/cmc.2023.031936 - 31 October 2022

    Abstract Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. More >

  • Open Access

    ARTICLE

    Brain Tumor Classification Using Image Fusion and EFPA-SVM Classifier

    P. P. Fathimathul Rajeena1,*, R. Sivakumar2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2837-2855, 2023, DOI:10.32604/iasc.2023.030144 - 17 August 2022

    Abstract An accurate and early diagnosis of brain tumors based on medical imaging modalities is of great interest because brain tumors are a harmful threat to a person’s health worldwide. Several medical imaging techniques have been used to analyze brain tumors, including computed tomography (CT) and magnetic resonance imaging (MRI). CT provides information about dense tissues, whereas MRI gives information about soft tissues. However, the fusion of CT and MRI images has little effect on enhancing the accuracy of the diagnosis of brain tumors. Therefore, machine learning methods have been adopted to diagnose brain tumors in… More >

  • Open Access

    ARTICLE

    Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

    R. Vijay Anandh1,*, S. Rukmani Devi2

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1897-1910, 2023, DOI:10.32604/csse.2023.027187 - 01 August 2022

    Abstract The demand for the exploration of ocean resources is increasing exponentially. Underwater image data plays a significant role in many research areas. Despite this, the visual quality of underwater images is degraded because of two main factors namely, backscattering and attenuation. Therefore, visual enhancement has become an essential process to recover the required data from the images. Many algorithms had been proposed in a decade for improving the quality of images. This paper aims to propose a single image enhancement technique without the use of any external datasets. For that, the degraded images are subjected… More >

  • Open Access

    ARTICLE

    Non Sub-Sampled Contourlet with Joint Sparse Representation Based Medical Image Fusion

    Kandasamy Kittusamy*, Latha Shanmuga Vadivu Sampath Kumar

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 1989-2005, 2023, DOI:10.32604/csse.2023.026501 - 01 August 2022

    Abstract Medical Image Fusion is the synthesizing technology for fusing multimodal medical information using mathematical procedures to generate better visual on the image content and high-quality image output. Medical image fusion represents an indispensible role in fixing major solutions for the complicated medical predicaments, while the recent research results have an enhanced affinity towards the preservation of medical image details, leaving color distortion and halo artifacts to remain unaddressed. This paper proposes a novel method of fusing Computer Tomography (CT) and Magnetic Resonance Imaging (MRI) using a hybrid model of Non Sub-sampled Contourlet Transform (NSCT) and… More >

  • Open Access

    ARTICLE

    Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

    Nguyen Tu Trung1,*, Tran Thi Ngan1, Tran Manh Tuan1, To Huu Nguyen2

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 535-544, 2023, DOI:10.32604/csse.2023.026011 - 01 June 2022

    Abstract Fusing medical images is a topic of interest in processing medical images. This is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis accuracy. This fusion aims to improve the image quality and preserve the specific features. The methods of medical image fusion generally use knowledge in many different fields such as clinical medicine, computer vision, digital imaging, machine learning, pattern recognition to fuse different medical images. There are two main approaches in fusing image, including spatial domain approach and transform domain approachs. This paper proposes a new… More >

  • Open Access

    ARTICLE

    No-Reference Stereo Image Quality Assessment Based on Transfer Learning

    Lixiu Wu1,*, Song Wang2, Qingbing Sang3

    Journal of New Media, Vol.4, No.3, pp. 125-135, 2022, DOI:10.32604/jnm.2022.027199 - 13 June 2022

    Abstract In order to apply the deep learning to the stereo image quality evaluation, two problems need to be solved: The first one is that we have a bit of training samples, another is how to input the dimensional image’s left view or right view. In this paper, we transfer the 2D image quality evaluation model to the stereo image quality evaluation, and this method solves the first problem; use the method of principal component analysis is used to fuse the left and right views into an input image in order to solve the second problem. More >

  • Open Access

    ARTICLE

    Enhanced Robotic Vision System Based on Deep Learning and Image Fusion

    E. A. Alabdulkreem1, Ahmed Sedik2, Abeer D. Algarni3,*, Ghada M. El Banby4, Fathi E. Abd El-Samie3,5, Naglaa F. Soliman3,6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1845-1861, 2022, DOI:10.32604/cmc.2022.023905 - 18 May 2022

    Abstract Image fusion has become one of the interesting fields that attract researchers to integrate information from different image sources. It is involved in several applications. One of the recent applications is the robotic vision. This application necessitates image enhancement of both infrared (IR) and visible images. This paper presents a Robot Human Interaction System (RHIS) based on image fusion and deep learning. The basic objective of this system is to fuse visual and IR images for efficient feature extraction from the captured images. Then, an enhancement model is carried out on the fused image to More >

  • Open Access

    ARTICLE

    Fuzzy Based Hybrid Focus Value Estimation for Multi Focus Image Fusion

    Muhammad Ahmad1,*, M. Arfan Jaffar1, Fawad Nasim1, Tehreem Masood1, Sheeraz Akram2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 735-752, 2022, DOI:10.32604/cmc.2022.019691 - 03 November 2021

    Abstract

    Due to limited depth-of-field of digital single-lens reflex cameras, the scene content within a limited distance from the imaging plane remains in focus while other objects closer to or further away from the point of focus appear as blurred (out-of-focus) in the image. Multi-Focus Image Fusion can be used to reconstruct a fully focused image from two or more partially focused images of the same scene. In this paper, a new Fuzzy Based Hybrid Focus Measure (FBHFM) for multi-focus image fusion has been proposed. Optimal block size is very critical step for multi-focus image fusion.

    More >

  • Open Access

    ARTICLE

    Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network

    Kanika Bhalla1, Deepika Koundal2,*, Surbhi Bhatia3, Mohammad Khalid Imam Rahmani4, Muhammad Tahir4

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5503-5518, 2022, DOI:10.32604/cmc.2022.021125 - 11 October 2021

    Abstract Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared (IR)/visible (VS) images. Dissimilarities in various kind of features in these images are vital to preserve in the single fused image. Hence, simultaneous preservation of both the aspects at the same time is a challenging task. However, most of the existing methods utilize the manual extraction of features; and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image. Therefore, this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images.… More >

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