185 research outputs found
Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising
The spatio-spectral total variation (SSTV) model has been widely used as an
effective regularization of hyperspectral images (HSI) for various applications
such as mixed noise removal. However, since SSTV computes local spatial
differences uniformly, it is difficult to remove noise while preserving complex
spatial structures with fine edges and textures, especially in situations of
high noise intensity. To solve this problem, we propose a new TV-type
regularization called Graph-SSTV (GSSTV), which generates a graph explicitly
reflecting the spatial structure of the target HSI from noisy HSIs and
incorporates a weighted spatial difference operator designed based on this
graph. Furthermore, we formulate the mixed noise removal problem as a convex
optimization problem involving GSSTV and develop an efficient algorithm based
on the primal-dual splitting method to solve this problem. Finally, we
demonstrate the effectiveness of GSSTV compared with existing HSI
regularization models through experiments on mixed noise removal. The source
code will be available at https://www.mdi.c.titech.ac.jp/publications/gsstv.Comment: Accepted to IEEE Geoscience and Remote Sensing Letters. The code is
available at https://www.mdi.c.titech.ac.jp/publications/gsst
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
Hyperspectral imaging can help better understand the characteristics of
different materials, compared with traditional image systems. However, only
high-resolution multispectral (HrMS) and low-resolution hyperspectral (LrHS)
images can generally be captured at video rate in practice. In this paper, we
propose a model-based deep learning approach for merging an HrMS and LrHS
images to generate a high-resolution hyperspectral (HrHS) image. In specific,
we construct a novel MS/HS fusion model which takes the observation models of
low-resolution images and the low-rankness knowledge along the spectral mode of
HrHS image into consideration. Then we design an iterative algorithm to solve
the model by exploiting the proximal gradient method. And then, by unfolding
the designed algorithm, we construct a deep network, called MS/HS Fusion Net,
with learning the proximal operators and model parameters by convolutional
neural networks. Experimental results on simulated and real data substantiate
the superiority of our method both visually and quantitatively as compared with
state-of-the-art methods along this line of research.Comment: 10 pages, 7 figure
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping
Hyperspectral images (HSIs) are inevitably degraded by a mixture of various
types of noise, such as Gaussian noise, impulse noise, stripe noise, and dead
pixels, which greatly limits the subsequent applications. Although various
denoising methods have already been developed, accurately recovering the
spatial-spectral structure of HSIs remains a challenging problem to be
addressed. Furthermore, serious stripe noise, which is common in real HSIs, is
still not fully separated by the previous models. In this paper, we propose an
adaptive hyperLaplacian regularized low-rank tensor decomposition (LRTDAHL)
method for HSI denoising and destriping. On the one hand, the stripe noise is
separately modeled by the tensor decomposition, which can effectively encode
the spatial-spectral correlation of the stripe noise. On the other hand,
adaptive hyper-Laplacian spatial-spectral regularization is introduced to
represent the distribution structure of different HSI gradient data by
adaptively estimating the optimal hyper-Laplacian parameter, which can reduce
the spatial information loss and over-smoothing caused by the previous total
variation regularization. The proposed model is solved using the alternating
direction method of multipliers (ADMM) algorithm. Extensive simulation and
real-data experiments all demonstrate the effectiveness and superiority of the
proposed method
A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization
We propose a new constrained optimization approach to hyperspectral (HS)
image restoration. Most existing methods restore a desirable HS image by
solving some optimization problem, which consists of a regularization term(s)
and a data-fidelity term(s). The methods have to handle a regularization
term(s) and a data-fidelity term(s) simultaneously in one objective function,
and so we need to carefully control the hyperparameter(s) that balances these
terms. However, the setting of such hyperparameters is often a troublesome task
because their suitable values depend strongly on the regularization terms
adopted and the noise intensities on a given observation. Our proposed method
is formulated as a convex optimization problem, where we utilize a novel hybrid
regularization technique named Hybrid Spatio-Spectral Total Variation (HSSTV)
and incorporate data-fidelity as hard constraints. HSSTV has a strong ability
of noise and artifact removal while avoiding oversmoothing and spectral
distortion, without combining other regularizations such as low-rank
modeling-based ones. In addition, the constraint-type data-fidelity enables us
to translate the hyperparameters that balance between regularization and
data-fidelity to the upper bounds of the degree of data-fidelity that can be
set in a much easier manner. We also develop an efficient algorithm based on
the alternating direction method of multipliers (ADMM) to efficiently solve the
optimization problem. Through comprehensive experiments, we illustrate the
advantages of the proposed method over various HS image restoration methods
including state-of-the-art ones.Comment: 20 pages, 4 tables, 10 figures, submitted to MDPI Remote Sensin
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