A Nonlocal Variational Model for Pansharpening Image Fusion

Duran, Joan; Buades, Antoni; Coll, Bartomeu; Sbert, Catalina

doi:10.1137/130928625

Cited by 65 publications

(38 citation statements)

References 47 publications

(65 reference statements)

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“…Further references about the nonlocal approach, targeted specifically towards the regularization of inverse problems in image processing, can be found e.g. in [24]- [31].…”

Section: A the Nonlocal Frameworkmentioning

confidence: 99%

Image Zoom Completion

Hidane

Gheche

Aujol

et al. 2016

IEEE Trans. on Image Process.

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We consider the problem of recovering a high-resolution image from a pair consisting of a complete low-resolution image and a high-resolution but incomplete one. We refer to this task as the image zoom completion problem. After discussing possible contexts in which this setting may arise, we introduce a nonlocal regularization strategy, giving full details concerning the numerical optimization of the corresponding energy and discussing its benefits and shortcomings. We also derive two total variation-based algorithms and evaluate the performance of the proposed methods on a set of natural and textured images. We compare the results and get with those obtained with two recent state-of-the-art single-image super-resolution algorithms.

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“…In this paper, we propose a new nonlocal variational model for the pansharpening of real satellite images. Compared to the previous work [20], no assumption on the co-registration of spectral data is made. Furthermore, a new constraint imposing the preservation of the radiometric ratio between the panchromatic and each spectral band is introduced, replacing the classical linearity assumption.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, P+XS functional incorporated an additional term according to which the panchromatic is a linear combination of the spectral components which are to be computed. Duran et al [20] proposed to keep the variational formulation introduced by Ballester et al [9] while incorporating nonlocal regularization that takes advantage of image self-similarities and leds to a significant reduction of color artifacts. In this setting, the panchromatic image is used to derive relationships among patches describing the geometry of the desired fused image.…”

Section: Introductionmentioning

confidence: 99%

A survey of pansharpening methods with a new band-decoupled variational model

Duran

Buades

Coll

et al. 2017

ISPRS Journal of Photogrammetry and Remote Sensing

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Most satellites decouple the acquisition of a panchromatic image at high spatial resolution from the acquisition of a multispectral image at lower spatial resolution. Pansharpening is a fusion technique used to increase the spatial resolution of the multispectral data while simultaneously preserving its spectral information. In this paper, we consider pansharpening as an optimization problem minimizing a cost function with a nonlocal regularization term. The energy functional which is to be minimized decouples for each band, thus permitting the application to misregistered spectral components. This requirement is achieved by dropping the, commonly used, assumption that relates the spectral and panchromatic modalities by a linear transformation. Instead, a new constraint that preserves the radiometric ratio between the panchromatic and each spectral component is introduced. An exhaustive performance comparison of the proposed fusion method with several classical and state-of-the-art pansharpening techniques illustrates its superiority in preserving spatial details, reducing color distortions, and avoiding the creation of aliasing artifacts.

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“…In this setting, Palsson, Sveinsson, and Ulfarsson (2014) presented afterwards a simple explicit image formation model with Tikhonov and TV regularization. More recently, a new variational method that incorporates a non-local regularization term and two fidelity terms was introduced by Duran et al (2014). Although the above-mentioned methods are promising, there is still some room for further improvement.…”

Section: Introductionmentioning

confidence: 99%

Pan-sharpening of multi-spectral images using a new variational model

Zhang

Fang

Zhou

et al. 2015

International Journal of Remote Sensing

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In the field of multi-spectral and panchromatic images fusion (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing accuracy. However, to the best of our knowledge, existing research works are mainly based on simple and flat networks with relatively shallow architecture, which severely limited their performances. In this paper, the concept of residual learning has been introduced to form a very deep convolutional neural network to make a full use of the high non-linearity of deep learning models. By both quantitative and visual assessments on a large number of high quality multi-spectral images from various sources, it has been supported that our proposed model is superior to all mainstream algorithms included in the comparison, and achieved the highest spatial-spectral unified accuracy.

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“…Then, a regularized constrained least square is used to restore the pansharpened image. The current sparse representation (SR)-based methods [16]- [19] and the nonlocal variational method [20] also belong to the model-based optimization method. In the SR-based methods, the observation model is defined with sparsity prior, and the pansharpened result is reconstructed utilizing the sparse signal reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Pansharpening With Multiscale Normalized Nonlocal Means Filter: A Two-Step Approach

Yin

2015

IEEE Trans. Geosci. Remote Sensing

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Pansharpening aims to synthesize a high-spatial-resolution multispectral (MS) image by fusing a panchromatic (PAN) image and a low-resolution MS image. The multiresolution analysis (MRA)-based methods are a popular group of pansharpening methods. However, in the MRA-based methods, spatial distortions may occur in the pansharpened product due to the misalignment of PAN and MS data. To address the spatial distortion issue in MRA-based methods, this paper proposes a two-step approach, which consists of the coarse step and the refined step. The coarse step produces a preliminary result using the traditional details injection model. Then, the preliminary product is refined with a second details injection operation in the refined step. Moreover, in our proposed two-step approach, a novel multiscale decomposition based on a normalized nonlocal means (NNLM) filter is developed to extract the spatial detail. Compared with the original nonlocal means filter, the designed NNLM makes the similarity measure more robust and accurate by exploiting the normalized intensity value and the mean value jointly. The experimental results on various satellite data demonstrate the superiority of the proposed pansharpening scheme by comparing with ten well-known methods.Index Terms-Multiresolution analysis (MRA), normalized nonlocal means (NNLM) filter, pansharpening, two-step approach.

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A Nonlocal Variational Model for Pansharpening Image Fusion

Cited by 65 publications

References 47 publications

Image Zoom Completion

A survey of pansharpening methods with a new band-decoupled variational model

Pan-sharpening of multi-spectral images using a new variational model

Pansharpening With Multiscale Normalized Nonlocal Means Filter: A Two-Step Approach

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