Image Fusion With Cosparse Analysis Operator

Gao, Rui; Vorobyov, Sergiy A.; Zhao, Hong

doi:10.1109/lsp.2017.2696055

Cited by 42 publications

(27 citation statements)

References 33 publications

(25 reference statements)

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“…There are also many algorithms based on combining SR and other tools including pulse coupled neural network(PCNN) [28], lowrank representation(LRR) [18] and shearlet transform [54]. Other options include the joint sparse representation [23] and cosparse representation [7].…”

Section: Introductionmentioning

confidence: 99%

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

332

149

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Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent lowrank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We build a novel image fusion framework based on MDLatLRR which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.

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Section: Introductionmentioning

confidence: 99%

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Kittler

2020

IEEE Trans. on Image Process.

332

149

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“…8 show the reconstructed image obtained with the proposed fusion method for a compression ratio of 0.4, which can be compared to the ground truth and the reconstruction obtained without CS. These results are compared with other fusion methods designed for compressed measurements [32] or for non-compressed data [14], [33]. 1 The method studied in [14] referred to as "Sparse fusion" exploits the fact that HS images live in a low dimensional subspace defined by the endmembers whereas the method of [32] is based on a multi-resolution analysis and a simple maximum selection fusion rule.…”

Section: Fusion Resultsmentioning

confidence: 99%

“…1 The method studied in [14] referred to as "Sparse fusion" exploits the fact that HS images live in a low dimensional subspace defined by the endmembers whereas the method of [32] is based on a multi-resolution analysis and a simple maximum selection fusion rule. The method of [33] is adapted to multiple multi-focus images. It is based on a sparse model and formulates the fusion problem as an inverse-problem regularized with a cosparsity prior in order to estimate an all-in-focus image.…”

Section: Fusion Resultsmentioning

confidence: 99%

“…It is based on a sparse model and formulates the fusion problem as an inverse-problem regularized with a cosparsity prior in order to estimate an all-in-focus image. Since the methods of [32] and [33] require the images to have the same size, the MS and HS images were interpolated to have the same size before applying these methods. Note that the images displayed elements of the matrix T + ρI N 2 L , with ∈ R m×n .…”

Section: Fusion Resultsmentioning

confidence: 99%

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Spectral Image Fusion From Compressive Measurements

Vargas

Espitia

Argüello

et al. 2019

IEEE Trans. on Image Process.

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Compressive spectral imagers reduce the number of sampled pixels by coding and combining the spectral information. However, sampling compressed information with simultaneous high spatial and high spectral resolution demands expensive high-resolution sensors. This paper introduces a model allowing data from high spatial/low spectral and low spatial/high spectral resolution compressive sensors to be fused. Based on titis model, the compressive fusion process is formulated as an inverse problem that minimizes an objective function defined as the sum of a quadratic data fidelity terin and smoothness and sparsity regularization penalties. The parameters of the different sensors are optimized and the choice of an appropriate regularization is studied in order to improve the quality of the high resolution reconstructed images. Simulation results conducted on synthetic and real data, with different compressive sampling imagers, allow the quality of the proposed fusion method to be appreciated.

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“…The problem is of high importance in many fields, ranging from remote sensing to medical imaging [9]- [12], especially for addressing the demand for cost minimization of optical sensors/cameras. Looking at recent approaches, sparsity and overcompleteness have been successfully used for computational image fusion [13]- [19]. The methods exploit the fact that patches of natural image can be compactly represented using an overcomplete dictionary as a linear combination of only few atoms.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion using Joint Sparse Representations and Coupled Dictionary Learning

Veshki

Ouzir

Vorobyov

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We address the multi-focus image fusion problem, where multiple images captured with different focal settings are to be fused into an all-in-focus image of higher quality. Algorithms for this problem necessarily admit the source image characteristics along with focused and blurred features. However, most sparsity-based approaches use a single dictionary in focused feature space to describe multifocus images, and ignore the representations in blurred feature space. We propose a multi-focus image fusion approach based on sparse representation using a coupled dictionary. It exploits the observations that the patches from a given training set can be sparsely represented by a couple of overcomplete dictionaries related to the focused and blurred categories of images and that a sparse approximation based on such coupled dictionary leads to a more flexible and therefore better fusion strategy than the one based on just selecting the sparsest representation in the original image estimate. In addition, to improve the fusion performance, we employ a coupled dictionary learning approach that enforces pairwise correlation between atoms of dictionaries learned to represent the focused and blurred feature spaces. We also discuss the advantages of the fusion approach based on coupled dictionary learning, and present efficient algorithms for fusion based on coupled dictionary learning. Extensive experimental comparisons with state-of-the-art multi-focus image fusion algorithms validate the effectiveness of the proposed approach. Index TermsSparse representations, coupled dictionary learning, image fusion, multi-focus image.The authors are with Aalto University, Dept. Signal Processing and Acoustics, FI-00076, AALTO, Finland. E-mails: farshad.ghorbaniveshki@aalto.fi and svor@ieee.org

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Image Fusion With Cosparse Analysis Operator

Cited by 42 publications

References 33 publications

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

MDLatLRR: A Novel Decomposition Method for Infrared and Visible Image Fusion

Spectral Image Fusion From Compressive Measurements

Image Fusion using Joint Sparse Representations and Coupled Dictionary Learning

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