Joint image fusion and denoising via three-layer decomposition and sparse representation

Li, Xiaosong; Zhou, Fuqiang; Tan, Haishu

doi:10.1016/j.knosys.2021.107087

Cited by 70 publications

(38 citation statements)

References 32 publications

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“…In equation ( 6), if r � x − b, then r can be viewed as an observation of some noise of x [8,11,22,23]. b represents residual, and u represents denoised image.…”

Section: Texture Inpainting Methodmentioning

confidence: 99%

A Criminisi-DnCNN Model-Based Image Inpainting Method

Zhu

Wang

2022

Mathematical Problems in Engineering

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Existing image inpainting methods achieve unideal results in dealing with centralized inpainting areas. For this reason, in this study, a Criminisi-DnCNN model-based image inpainting method is proposed. Inspired by the manual inpainting technology, the pointwise mutual information (PMI) algorithm was adopted to obtain the marginal structural map of the images to be repaired. Then, the Criminisi algorithm was used to restore the marginal structure to obtain the complete marginal structure image guided by the superficial linear structure. Finally, the problem of texture inpainting was converted into the counterpart of image denoising through the separation of variables by using the denoising convolutional neural network image denoiser (DnCNN). Compared with the existing inpainting methods, this model has improved the clarity of the marginal structure and reduced the blurring of the area to be repaired.

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“…In equation ( 6), if r � x − b, then r can be viewed as an observation of some noise of x [8,11,22,23]. b represents residual, and u represents denoised image.…”

Section: Texture Inpainting Methodmentioning

confidence: 99%

A Criminisi-DnCNN Model-Based Image Inpainting Method

Zhu

Wang

2022

Mathematical Problems in Engineering

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“…At present, there are many kinds of network models, among which lenet, alexnet, googlenet, etc. ese kinds of network models mainly classify focused and defocused images [14].…”

Section: Image Fusion Algorithm Based On Deep Learningmentioning

confidence: 99%

Research on Low-Resolution Image Fusion Algorithm Based on Deep Learning

Zhang

2022

Scientific Programming

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The emergence of portable devices provides great convenience for image acquisition, but the image resolution is too low, which affects the identification and use of the image. Multiframe super-resolution algorithm makes a great contribution to extracting image features, but there is a problem with too much computation. Based on this, this paper proposes a low-resolution image fusion algorithm based on deep learning. Based on the introduction of extracting image features from low-resolution images, this paper proposes to fuse multiple low-resolution images, reduce the time of calculation error per frame, reduce the amount of calculation, extract feature information, and improve the alexnet network model to realize image extraction. The convolutional self-coding fusion network is used to realize image fusion and complete the reconstruction and fusion of low-resolution images. In the simulation analysis of the fusion algorithm, objective indexes such as network structure and loss function are selected to evaluate the effectiveness of the algorithm. This paper reduces the computational complexity of image acquisition and ensures that the amount of calculation can be reduced in each iteration. Analyze the effectiveness of the fusion algorithm, comprehensively evaluate the objective indicators, and verify the advantages of the algorithm through indicators such as gradient change and information entropy.

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“…Medical Image fusion technology integrates technologies in many fields such as computer technology, sensor technology, artificial intelligence, and image processing. It comprehensively extracts image information collected by different sensors and concentrates all the information of the image, which can reduce the information redundancy of the image, enhance the readability of the image and provide more specific disease information for diagnosis ( Riboni and Murtas, 2019 ; Li et al, 2021 ; Wang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Medical Image Fusion With Geometric Algebra Based Sparse Representation

Fang

Wang

et al. 2022

Front. Genet.

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Multi-modal medical image fusion can reduce information redundancy, increase the understandability of images and provide medical staff with more detailed pathological information. However, most of traditional methods usually treat the channels of multi-modal medical images as three independent grayscale images which ignore the correlation between the color channels and lead to color distortion, attenuation and other bad effects in the reconstructed image. In this paper, we propose a multi-modal medical image fusion algorithm with geometric algebra based sparse representation (GA-SR). Firstly, the multi-modal medical image is represented as a multi-vector, and the GA-SR model is introduced for multi-modal medical image fusion to avoid losing the correlation of channels. Secondly, the orthogonal matching pursuit algorithm based on geometric algebra (GAOMP) is introduced to obtain the sparse coefficient matrix. The K-means clustering singular value decomposition algorithm based on geometric algebra (K-GASVD) is introduced to obtain the geometric algebra dictionary, and update the sparse coefficient matrix and dictionary. Finally, we obtain the fused image by linear combination of the geometric algebra dictionary and the coefficient matrix. The experimental results demonstrate that the proposed algorithm outperforms existing methods in subjective and objective quality evaluation, and shows its effectiveness for multi-modal medical image fusion.

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Joint image fusion and denoising via three-layer decomposition and sparse representation

Cited by 70 publications

References 32 publications

A Criminisi-DnCNN Model-Based Image Inpainting Method

A Criminisi-DnCNN Model-Based Image Inpainting Method

Research on Low-Resolution Image Fusion Algorithm Based on Deep Learning

Multi-Modal Medical Image Fusion With Geometric Algebra Based Sparse Representation

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