Image Fusion Based on the \({\Delta ^{ - 1}} - T{V_0}\) Energy Function

Xie, Qiwei; Ma, Chao; Guo, Chunzhao; John, Vijay; Mita, Seiichi; Qian, Long

doi:10.3390/e16116099

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…In our experiment, λ 0 , λ max , κ 1 , ϕ are set to 0.09, 100, 1.5, 5, respectively. This method has been proved to be effective in accelerating convergence [31][32][33][34].…”

Section: P Sparse Regularization Optimization Modelmentioning

confidence: 99%

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

Zhao

Chen

et al. 2019

Remote Sensing

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Known as input in the Numerical Weather Prediction (NWP) models, Microwave Radiation Imager (MWRI) data have been widely distributed to the user community. With the development of remote sensing technology, improving the geolocation accuracy of MWRI data are required and the first step is to estimate the geolocation error accurately. However, the traditional method, such as the coastline inflection method (CIM), usually has the disadvantages of low accuracy and poor anti-noise ability. To overcome these limitations, this paper proposes a novel ℓ p iterative closest point coastline inflection method ( ℓ p -ICP CIM). It assumes that the field of views (FOVs) across the coastline can degenerate into a step function and employs an ℓ p ( 0 ≤ p < 1 ) sparse regularization optimization model to solve the coastline point. After estimating the coastline points, the ICP algorithm is employed to estimate the corresponding relationship between the estimated coastline points and the real coastline. Finally, the geolocation error can be defined as the distance between the estimated coastline point and the corresponding point on the true coastline. Experimental results on simulated and real data sets show the effectiveness of our method over CIM. The accuracy of the geolocation error estimated by ℓ p -ICP CIM is up to 0 . 1 pixel, in more than 90 % of cases. We also show that the distribution of brightness temperature near the coastline is more consistent with the real coastline and the average geolocation error is reduced by 63 % after geolocation error correction.

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“…In our experiment, λ 0 , λ max , κ 1 , ϕ are set to 0.09, 100, 1.5, 5, respectively. This method has been proved to be effective in accelerating convergence [31][32][33][34].…”

Section: P Sparse Regularization Optimization Modelmentioning

confidence: 99%

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

Zhao

Chen

et al. 2019

Remote Sensing

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“…VO methods approach pan-sharpening as an inverse optimization problem whereby a fused image is derived by generating an energy functional from the input MS and PAN images that is then passed into an optimization algorithm. In other words, VO methods focus on building the most effective and suitable models to characterize the correlation between the spectral information from the MS image with the spatial information in the PAN image [5,[7][8][9]. A review of current industrystandard GIS software revealed that VO methods have yet to be widely adopted.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework

2022

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Pan-sharpening is a pixel-level image fusion process whereby a lower-spatial-resolution multispectral image is merged with a higher-spatial-resolution panchromatic one. One of the drawbacks of this process is that it may introduce spectral or radiometric distortion. The degree to which distortion is introduced is dependent on the imaging sensor, the pan-sharpening algorithm employed, and the context of the scene analyzed. Studies that evaluate the quality of pan-sharpening algorithms often fail to account for changes in geographic context and are agnostic to any specific applications of an end user. This research proposes an evaluation framework to assess the effects of six widely used pan-sharpening algorithms on normalized difference vegetation index (NDVI) calculation in five contextually diverse geographic locations. Output image quality is assessed by comparing the empirical cumulative density function of NDVI values that are calculated by using pre-sharpened and sharpened imagery. The premise is that an effective algorithm will generate a sharpened multispectral image with a cumulative NDVI distribution that is similar to the pre-sharpened image. Research results revealed that, generally, the Gram–Schmidt algorithm introduces a significant degree of spectral distortion regardless of sensor and spatial context. In addition, higher-spatial-resolution imagery is more susceptible to spectral distortions upon pan-sharpening. Furthermore, variability in cumulative density of spectral information in fused images justifies the application of an analytical framework to assist users in selecting the most effective methods for their intended application.

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“…It has been shown in [29] that under certain RIP condition of A, L q -norm minimization algorithms require fewer sampling data but gain a better recovery performance than L 1 -norm minimization algorithms. Moreover, the sufficient conditions in terms of RIP for L qnorm minimization are weaker than those for L 1 -norm minimization [30,31]. However, in general, relative to L 1 -norm minimization, L q -norm minimization is more difficult to directly tackle due to its nonsmoothing.…”

Section: Introductionmentioning

confidence: 99%

A New Non-Convex Approach for Compressive Sensing Mri

Yue¹,

Yin²

2020

PIER C

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Compressive sensing (CS) is an effective method for reconstructing magnetic resonance imaging (MRI) image from under-determined linear system (ULS). However, how to improve the accuracy of MRI image reconstructed by CS is still a serious problem, especially in noisy conditions. To solve this problem, in this paper, we propose a novel approach, dubbed as regularized maximum entropy function (RMEF) minimization algorithm. Specifically, motivated by the entropy function in information theory, we propose a maximum entropy function (MEF) to approximate L q-norm (0 < q < 1) as sparsity promoting objectives, and then the regularization mechanism for improving the de-noising performance is adopted. Combining the above two ideas, a new objective function of RMEF method is proposed, and the global minimum is iteratively solved. We further analyze the convergence to verify the robustness of the RMEF algorithm. Experiments demonstrate the state-ofthe-art performances of the proposed RMEF algorithm and show that the RMEF achieves higher PSNR and SSIM than other widely-adopted methods in MRI image recovery.

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Image Fusion Based on the \({\Delta ^{ - 1}} - T{V_0}\) Energy Function

Cited by 4 publications

References 32 publications

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

ℓp-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

Performance Evaluation of Multiple Pan-Sharpening Techniques on NDVI: A Statistical Framework

A New Non-Convex Approach for Compressive Sensing Mri

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