Structure-aware image fusion

Wen, Li; Xie, Yuange; Zhou, Haole; Han, Ying; Zhan, Kun

doi:10.1016/j.ijleo.2018.06.123

Cited by 76 publications

(28 citation statements)

References 45 publications

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“…According to the image fusion method propsed in Figure 4 and the based-on variance calculated in discrete cosine transform domain without consistency verification (DctVar) and based-on variance calculated in discrete cosine transform domain with consistency verification (DctVarCv) [12,13] and structure-aware image fusion (SAIF) [11] methods, comparison and verification were carried out. This study was to evaluate the quality of image fusion using relevant indicators compiled by Liu et al [42].…”

Section: Image Fusion Resultsmentioning

confidence: 99%

“…On the contrary, if too many decomposition stages are used, spatial similarity between the fused image and the original will be poorer [10]. Among those fusion methods, structure-aware image fusion [11] and image fusion in the discrete cosine transform (DCT) domain [12,13] are quite classic methods and widely used in various fields [14,15]. The following discusses wavelet-based image fusion in recent years.…”

Section: Introductionmentioning

confidence: 99%

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Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

Jian

Chu

et al. 2020

Applied Sciences

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This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.

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Section: Image Fusion Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

Jian

Chu

et al. 2020

Applied Sciences

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“…The dataset used in the experiments is the TNO Image Fusion Dataset, which contains registered infraredvisible image pairs in various conditions. The performance of the algorithm is tested by direct observation and comparison with seven excellent fusion algorithms, they are DenseFuse [9], MDLatLRR [32], VSM-WLS [2], SAF [33], CSR [6], FPDE [19], GTF [20].…”

Section: B Experimental Results and Comparisonmentioning

confidence: 99%

Infrared and Visible Image Fusion Based on Gradient Transfer Optimization Model

2020

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To tackle the problem of partial loss of image details in infrared and visible image fusion, a gradient transfer optimization model is proposed for the fusion of infrared and visible images. Firstly, an adaptive image decomposition method is proposed based on coupled partial differential equation, the infrared image and the visible image are decomposed into base layer and detail layer to extract the highbrightness target and the details of the two images. Based on this superior information of infrared image and visible image, the optimization model is designed to obtain the fusion image obvious target and rich details. For the proposed optimization model, Alternating Direction Method of Multipliers (ADMM) is used to decompose the original model into sub-problems that are easy to solve and iteratively optimize to obtain the optimal solution. The introduction of control parameters makes the model more flexible in different situations, and retains the thermal radiation information of the infrared image and the detailed information of the visible image to the greatest extent. The fused image visual effects and performance indicators are improved. We completed the experiment using a public data set and analyzed the experimental results. The experimental results show that the proposed method can better preserve the clear target and texture information of infrared and visible images, and the fusion results are more accurate and comprehensive. The experiment results also indicate that our method performs well and achieves comparable metric values with the state-of-the-art methods. INDEX TERMS Image fusion, optimization model, ADMM, partial differential equation.

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“…In the process of Image fusion two or more images are combines with different focus values of the same picture using different fusion rules. The composite image obtained after fusion called as all-in-focus image gives more information and is more convenient to visual perception which can be further processed for the object and target detection [1][2][3][4][5]. Generally the intention of the fusion process is to combine the redundant and complementary information from multiple images to create a composite fused image that contain better properties than individual input images in terms of visual perception and suitable for secondary processing.…”

Section: Introductionmentioning

confidence: 99%

Advanced Multifocus Image Fusion algorithm using FPDCT with Modified PCA

Mohan¹,

Kiran²,

Kumar³

2019

IJITEE

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 Abstract: Image fusion has been performed and reported in this paper for multi-focused images using Frequency Partition Discrete Cosine Transform (FP-DCT) with Modified Principal component analysis (MPCA) technique. The image fusion with decomposition at fixed levels may be treated as a very critical rule in the earlier image processing techniques. The frequency partitioning approach was used in this study to select the decomposition levels based on the pixel intensity and clarity. This paper also presents the modified PCA technique which provides dimensionality reduction. The wide range of quality evaluation metrics was computed to compare the fusion performance on the five images. Different techniques such as PCA, wavelet transforms with PCA, Multiresolution Singular Value Decomposition (MSVD) with PCA, Multiresolution DCT (MRDCT) with PCA, Frequency partitioning DCT (FP-DCT) with PCA were computed for comparison with the proposed FP-DCT Modified PCA (MPCA) technique. Images obtained after fusion process obtained by the method proposed shows enhanced visual quality, negligible information loss and discontinuities in the image than compared to other state of the art methods.

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Structure-aware image fusion

Cited by 76 publications

References 45 publications

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

Infrared and Visible Image Fusion Based on Gradient Transfer Optimization Model

Advanced Multifocus Image Fusion algorithm using FPDCT with Modified PCA

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