Fast filtering image fusion

Zhan, Kun; Xie, Yuange; Wang, Haibo; Min, Yufang

doi:10.1117/1.jei.26.6.063004

Cited by 55 publications

(23 citation statements)

References 37 publications

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“…In order to prove the superiority of the proposed method, we compare it with 13 other image fusion methods here. The comparative methods include the adaptive sparse representation (ASR) [ 35 ], the convolutional sparse representation (CSR) [ 36 ], curvelet transform (CVT) [ 37 ], dual-tree complex wavelet transform (DTCWT) [ 8 ], the gradient transfer fusion (GTF) [ 38 ], the hybrid multi-scale decomposition (H-MSD) [ 39 ], the convolutional neural network (CNN) [ 40 ], Laplacian pyramid (LP) [ 5 ], the general framework based on multi-scale transform and sparse representation (MSSR) [ 34 ], the multi-resolution singular value decomposition (MSVD) [ 41 ], nonsubsampled contourlet transform (NSCT) [ 9 ], the visual saliency map and weighted least square optimization (WLS) [ 42 ] and the fast filtering image fusion (FFIF) [ 43 ]. All these methods were given default parameters from in their related papers.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Yudan

Yang

Zhang

et al. 2020

Entropy

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Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information ( M I )—5.3377, feature mutual information ( F M I )—0.5600, normalized weighted edge preservation value ( Q A B / F )—0.6978 and nonlinear correlation information entropy ( N C I E )—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Yudan

Yang

Zhang

et al. 2020

Entropy

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“…If one bit of the input image is altered, the NPCR and UACI of traditional methods, in CCAES [41], CDCP [42], and CHC [43] are close to hypothetical values. DNA-based methods, C-DNA [44] and HC-DNA [45], have a better noise attack performance than the previous works. Furthermore, the values are compared against the critical values as in [46,47].…”

mentioning

confidence: 85%

“…So, the presented cryptosystem attains high performance by getting NPCR values and UACI values near to their hypothetical values. [41] 99.5697 Successful Successful Successful CDCP [42] 100 Successful Successful Successful CHC [43] 99.6605 Successful Successful Successful C-DNA [44] 15.25 × 10 −4 NA NA NA HC-DNA [45] 59.7406 NA NA NA [41] 33.4767 Successful Successful Successful CDCP [42] 33.5752 Successful Successful Successful CHC [43] 33.4263 Successful Successful Successful C-DNA [44] 8.97 × 10 −6 NA NA NA HC-DNA [45] 25.0487 NA NA NA…”

mentioning

confidence: 99%

A Bijective Image Encryption System Based on Hybrid Chaotic Map Diffusion and DNA Confusion

Elkamchouchi

Mohamed

Moussa

2020

Entropy

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Modern multimedia communications technology requirements have raised security standards, which allows for enormous development in security standards. This article presents an innovative symmetric cryptosystem that depends on the hybrid chaotic Lorenz diffusion stage and DNA confusion stage. It involves two identical encryption and decryption algorithms, which simplifies the implementation of transmitting and receiving schemes of images securely as a bijective system. Both schemes utilize two distinctive non-consecutive chaotic diffusion stages and one DNA scrambling stage in between. The generation of the coded secret bit stream employs a hybrid chaotic system, which is employed to encrypt or decrypt the transmitted image and is utilized in the diffusion process to dissipate the redundancy in the original transmitted image statistics. The transmitted image is divided into eight scrambled matrices according to the position of the pixel in every splitting matrix. Each binary matrix is converted using a different conversion rule in the Watson–Crick rules. The DNA confusion stage is applied to increase the complexity of the correlation between the transmitted image and the utilized key. These stages allow the proposed image encryption scheme to be more robust against chosen/known plaintext attacks, differential attacks, cipher image attacks, and information entropy. The system was revealed to be more sensitive against minimal change in the generated secret key. The analysis proves that the system has superior statistical properties, bulkier key space, better plain text sensitivity, and improved key sensitivity compared with former schemes.

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“…They are pixel level, feature level and decision level. Successful fusion methods based on morphological operators are discussed in [6][7].…”

Section: Introductionmentioning

confidence: 99%

Medical Image Fusion Using Integrated Guided Nonlinear Anisotropic Filtering with Image Statistics

Reddy¹,

Reddy²,

Reddy³

2020

IJIES

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An image that is highly informative is produced by unifying the information from couple of or numerous source images which is referred as image fusion. It has been employing in most of the applications in the medical field like detecting of tumours, treating Alzheimer's, surgery of brain with the assistance of computer and some other clinical diagnosis. Successful diagnosis of specific diseases necessitates the enhancement in the exactness of fusion algorithms. For examining the body of human, the images obtained from magnetic resonance imaging (MRI) and computed tomography (CT) plays a vital role. Channelizing the maximum info from the source images to the fused image with understate loss of info that must mitigate the presence of artifacts in the fused outcome is the basic idea of any fusion methodology. In this context, a novel medical image fusion approach is implemented, that utilizes integrated guided and nonlinear anisotropic (IGNLA) filtering with image statistics. This approach upholds the info of texture in the fused images more efficaciously. In addition, proposed medical image fusion is extended for color images and applied to MR-Gad, MR-T2 and SPECT-Tc images. Extensive simulation results of proposed medical image fusion are compared with traditional and recent image fusion algorithms and disclose the superiority of proposed approach with respect to image quality metrics.

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Fast filtering image fusion

Cited by 55 publications

References 37 publications

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

Entropy-Based Image Fusion with Joint Sparse Representation and Rolling Guidance Filter

A Bijective Image Encryption System Based on Hybrid Chaotic Map Diffusion and DNA Confusion

Medical Image Fusion Using Integrated Guided Nonlinear Anisotropic Filtering with Image Statistics

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