775 research outputs found
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
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
Robust Constrained Hyperspectral Unmixing Using Reconstructed-Image Regularization
Hyperspectral (HS) unmixing is the process of decomposing an HS image into
material-specific spectra (endmembers) and their spatial distributions
(abundance maps). Existing unmixing methods have two limitations with respect
to noise robustness. First, if the input HS image is highly noisy, even if the
balance between sparse and piecewise-smooth regularizations for abundance maps
is carefully adjusted, noise may remain in the estimated abundance maps or
undesirable artifacts may appear. Second, existing methods do not explicitly
account for the effects of stripe noise, which is common in HS measurements, in
their formulations, resulting in significant degradation of unmixing
performance when such noise is present in the input HS image. To overcome these
limitations, we propose a new robust hyperspectral unmixing method based on
constrained convex optimization. Our method employs, in addition to the two
regularizations for the abundance maps, regularizations for the HS image
reconstructed by mixing the estimated abundance maps and endmembers. This
strategy makes the unmixing process much more robust in highly-noisy scenarios,
under the assumption that the abundance maps used to reconstruct the HS image
with desirable spatio-spectral structure are also expected to have desirable
properties. Furthermore, our method is designed to accommodate a wider variety
of noise including stripe noise. To solve the formulated optimization problem,
we develop an efficient algorithm based on a preconditioned primal-dual
splitting method, which can automatically determine appropriate stepsizes based
on the problem structure. Experiments on synthetic and real HS images
demonstrate the advantages of our method over existing methods.Comment: Submitted to IEEE Transactions on Geoscience and Remote Sensin
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization
The paper proposes a new high spatial resolution hyperspectral (HR-HS) image
estimation method based on convex optimization. The method assumes a low
spatial resolution HS (LR-HS) image and a guide image as observations, where
both observations are contaminated by noise. Our method simultaneously
estimates an HR-HS image and a noiseless guide image, so the method can utilize
spatial information in a guide image even if it is contaminated by heavy noise.
The proposed estimation problem adopts hybrid spatio-spectral total variation
as regularization and evaluates the edge similarity between HR-HS and guide
images to effectively use apriori knowledge on an HR-HS image and spatial
detail information in a guide image. To efficiently solve the problem, we apply
a primal-dual splitting method. Experiments demonstrate the performance of our
method and the advantage over several existing methods.Comment: Accepted to IEEE Transactions on Geoscience and Remote Sensin
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
Hyperspectral image is unique and useful for its abundant spectral bands, but
it subsequently requires extra elaborated treatments of the spatial-spectral
correlation as well as the global correlation along the spectrum for building a
robust and powerful HSI restoration algorithm. By considering such HSI
characteristics, 3D Quasi-Recurrent Neural Network (QRNN3D) is one of the HSI
denoising networks that has been shown to achieve excellent performance and
flexibility. In this paper, we show that with a few simple modifications, the
performance of QRNN3D could be substantially improved further. Our
modifications are based on the finding that through QRNN3D is powerful for
modeling spectral correlation, it neglects the proper treatment between
features from different sources and its training strategy is suboptimal. We,
therefore, introduce an adaptive fusion module to replace its vanilla additive
skip connection to better fuse the features of the encoder and decoder. We
additionally identify several important techniques to further enhance the
performance, which includes removing batch normalization, use of extra
frequency loss, and learning rate warm-up. Experimental results on various
noise settings demonstrate the effectiveness and superior performance of our
method.Comment: technical repor
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