Image fusion algorithm based on unsupervised deep learning-optimized sparse representation

2022 ◽  
Vol 71 ◽  
pp. 103140
Author(s):  
Feng-Ping An ◽  
Xing-min Ma ◽  
Lei Bai
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Sparse Representation ◽  
Fusion Algorithm ◽  
Unsupervised Deep Learning

scholarly journals Medical Image Fusion Using Deep Learning Mechanism

2022 ◽  
Vol 10 (1) ◽  
pp. 128-136
Author(s):  
Nukapeyyi Tanuja
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Sparse Representation ◽  
Medical Image ◽  
Medical Image Fusion ◽  
Morphological Component Analysis ◽  
Extension Mechanism ◽  
Morphological Component ◽  
Image Component ◽  
Segmentation Image

Abstract: Sparse representation(SR) model named convolutional sparsity based morphological component analysis is introduced for pixel-level medical image fusion. The CS-MCA model can achieve multicomponent and global SRs of source images, by integrating MCA and convolutional sparse representation(CSR) into a unified optimization framework. In the existing method, the CSRs of its gradient and texture components are obtained by the CSMCA model using pre-learned dictionaries. Then for each image component, sparse coefficients of all the source images are merged and then fused component is reconstructed using the corresponding dictionary. In the extension mechanism, we are using deep learning based pyramid decomposition. Now a days deep learning is a very demanding technology. Deep learning is used for image classification, object detection, image segmentation, image restoration. Keywords: CNN, CT, MRI, MCA, CS-MCA.

The infrared and visible image fusion algorithm based on target separation and sparse representation

2014 ◽  
Vol 67 ◽  
pp. 397-407 ◽  
Author(s):  
Xiaoqi Lu ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
He Liu ◽  
Haiquan Pei
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Fusion Algorithm ◽  
Visible Image

Deep learning supported disease detection with multi-modality image fusion

2021 ◽  
pp. 1-24
Author(s):  
F. Sangeetha Francelin Vinnarasi ◽  
Jesline Daniel ◽  
J.T. Anita Rose ◽  
R. Pugalenthi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Fusion ◽  
Clustering Algorithm ◽  
Medical Images ◽  
Disease Diagnosis ◽  
High Energy ◽  
Fusion Algorithm ◽  
Fused Image ◽  
Fuzzy C Means Clustering

Multi-modal image fusion techniques aid the medical experts in better disease diagnosis by providing adequate complementary information from multi-modal medical images. These techniques enhance the effectiveness of medical disorder analysis and classification of results. This study aims at proposing a novel technique using deep learning for the fusion of multi-modal medical images. The modified 2D Adaptive Bilateral Filters (M-2D-ABF) algorithm is used in the image pre-processing for filtering various types of noises. The contrast and brightness are improved by applying the proposed Energy-based CLAHE algorithm in order to preserve the high energy regions of the multimodal images. Images from two different modalities are first registered using mutual information and then registered images are fused to form a single image. In the proposed fusion scheme, images are fused using Siamese Neural Network and Entropy (SNNE)-based image fusion algorithm. Particularly, the medical images are fused by using Siamese convolutional neural network structure and the entropy of the images. Fusion is done on the basis of score of the SoftMax layer and the entropy of the image. The fused image is segmented using Fast Fuzzy C Means Clustering Algorithm (FFCMC) and Otsu Thresholding. Finally, various features are extracted from the segmented regions. Using the extracted features, classification is done using Logistic Regression classifier. Evaluation is performed using publicly available benchmark dataset. Experimental results using various pairs of multi-modal medical images reveal that the proposed multi-modal image fusion and classification techniques compete the existing state-of-the-art techniques reported in the literature.

scholarly journals A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset

2021 ◽  
Vol 7 ◽  
pp. e364
Author(s):  
Omar M. Elzeki ◽  
Mohamed Abd Elfattah ◽  
Hanaa Salem ◽  
Aboul Ella Hassanien ◽  
Mahmoud Shams
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Imaging Method ◽  
Fusion Algorithm ◽  
Algorithm Performance ◽  
Patient Status ◽  
Layer Image ◽  
Chest X Ray ◽  
Almost All

Background and Purpose COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people’s health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. Materials and Methods In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. Results Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. Conclusions A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

Multi-Modal Object Tracking and Image Fusion With Unsupervised Deep Learning

2019 ◽  
Vol 12 (8) ◽  
pp. 3056-3066 ◽  
Author(s):  
Nicholas LaHaye ◽  
Jordan Ott ◽  
Michael J. Garay ◽  
Hesham Mohamed El-Askary ◽  
Erik Linstead
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Object Tracking ◽  
Unsupervised Deep Learning

A Novel Image Fusion Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1593-1596
Author(s):  
Shan Shan Liu
Keyword(s):  
Wavelet Transform ◽  
Image Quality ◽  
Compressed Sensing ◽  
Image Fusion ◽  
Sparse Representation ◽  
Reconstructed Image ◽  
Fusion Algorithm

Based on the wavelet transform, this study introduced the theory of the compressed sensing algorithm. Then proposed a wavelet transform based compressed sensing algorithm by the better sparse representation ability of the wavelet transform on the image. Finally, this algorithm was compared with the DCT and wavelet transform algorithm. The experiment results show that the reconstructed image quality has a significant improvement. Especially, this algorithm has better effect on the images with rich curve.

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