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Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
Computer Vision and Pattern Recognition · Submitted 4 Oct 2017
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.
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The abstract, sentence by sentence
Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. | background 0.92 | 0.07 | 0.01 | asserted 1.00 |
| s2 | In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. | contribution 0.99 | 0.75 | 0.11 | asserted 0.98 |
| s3 | This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. | method 0.99 | 0.84 | 0.05 | asserted 1.00 |
| s4 | To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. | method 0.92 | 0.83 | 0.04 | asserted 0.90 |
| s5 | Using this approach, our model can realign the given images if needed. | method 0.59 | 0.67 | 0.06 | asserted 0.52 |
| s6 | Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. | result 0.87 | 0.95 | 0.25 | hedged possibility 0.63 |
| s7 | Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel. | result 0.73 | 0.96 | 0.64 | hedged possibility 0.89 |
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Original abstract
Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-registrations between the two images, we consider a non-convex blind super-resolution problem where both a fused image and the corresponding convolution kernel are estimated. Using this approach, our model can realign the given images if needed. Our experimental results indicate that the non-convexity is negligible in practice and that reliable solutions can be computed using a variety of different optimization algorithms. Numerical results on real remote sensing data from plant sciences and urban monitoring show the potential of the proposed method and suggests that it is robust with respect to the regularization parameters, mis-registration and the shape of the kernel.
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A potential solution to avoid such issues is the infimal convolution of Bregman distances, in this cases with the two normal fields and thus subgradients of opposite sign (cf. [ref] ) [equation] We also mention some other related approaches to modify total variation functionals such as the parallel level set models (cf. [ref] ), which can be related to the Bregman distance for total variation (cf. [ref] ), or directional / structural total variation (cf. [ref] ), formally [equation]
Modern Regularization Methods for Inverse Problems · Bregman Distances · 0.83
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This naturally leads to the directional total generalized variation which combines higher order smoothness with structural a-priori knowledge as presented in this work, thereby generalizing the definition of [ref] .
Directional Total Generalized Variation Regularization · Conclusions and Outlook · 0.98
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