11 research outputs found
Hyperspectral Image Restoration via Multi-mode and Double-weighted Tensor Nuclear Norm Minimization
Tensor nuclear norm (TNN) induced by tensor singular value decomposition
plays an important role in hyperspectral image (HSI) restoration tasks. In this
letter, we first consider three inconspicuous but crucial phenomenons in TNN.
In the Fourier transform domain of HSIs, different frequency components contain
different information; different singular values of each frequency component
also represent different information. The two physical phenomenons lie not only
in the spectral dimension but also in the spatial dimensions. Then, to improve
the capability and flexibility of TNN for HSI restoration, we propose a
multi-mode and double-weighted TNN based on the above three crucial
phenomenons. It can adaptively shrink the frequency components and singular
values according to their physical meanings in all modes of HSIs. In the
framework of the alternating direction method of multipliers, we design an
effective alternating iterative strategy to optimize our proposed model.
Restoration experiments on both synthetic and real HSI datasets demonstrate
their superiority against related methods
A General Destriping Framework for Remote Sensing Images Using Flatness Constraint
This paper proposes a general destriping framework using flatness
constraints, where we can handle various regularization functions in a unified
manner. Removing stripe noise, i.e., destriping, from remote sensing images is
an essential task in terms of visual quality and subsequent processing. Most of
the existing methods are designed by combining a particular image
regularization with a stripe noise characterization that cooperates with the
regularization, which precludes us to examine different regularizations to
adapt to various target images. To resolve this, we formulate the destriping
problem as a convex optimization problem involving a general form of image
regularization and the flatness constraints, a newly introduced stripe noise
characterization. This strong characterization enables us to consistently
capture the nature of stripe noise, regardless of the choice of image
regularization. For solving the optimization problem, we also develop an
efficient algorithm based on a diagonally preconditioned primal-dual splitting
algorithm (DP-PDS), which can automatically adjust the stepsizes. The
effectiveness of our framework is demonstrated through destriping experiments,
where we comprehensively compare combinations of image regularizations and
stripe noise characterizations using hyperspectral images (HSI) and infrared
(IR) videos.Comment: submitted to IEEE Transactions on Geoscience and Remote Sensin
Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Deep-learning-based hyperspectral image (HSI) restoration methods have gained
great popularity for their remarkable performance but often demand expensive
network retraining whenever the specifics of task changes. In this paper, we
propose to restore HSIs in a unified approach with an effective plug-and-play
method, which can jointly retain the flexibility of optimization-based methods
and utilize the powerful representation capability of deep neural networks.
Specifically, we first develop a new deep HSI denoiser leveraging gated
recurrent convolution units, short- and long-term skip connections, and an
augmented noise level map to better exploit the abundant spatio-spectral
information within HSIs. It, therefore, leads to the state-of-the-art
performance on HSI denoising under both Gaussian and complex noise settings.
Then, the proposed denoiser is inserted into the plug-and-play framework as a
powerful implicit HSI prior to tackle various HSI restoration tasks. Through
extensive experiments on HSI super-resolution, compressed sensing, and
inpainting, we demonstrate that our approach often achieves superior
performance, which is competitive with or even better than the state-of-the-art
on each task, via a single model without any task-specific training.Comment: code at https://github.com/Zeqiang-Lai/DPHSI
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications
This paper proposes a method of designing appropriate diagonal
preconditioners for a preconditioned primal-dual splitting method (P-PDS).
P-PDS can efficiently solve various types of convex optimization problems
arising in signal processing and image processing. Since the appropriate
diagonal preconditioners that accelerate the convergence of P-PDS vary greatly
depending on the structure of the target optimization problem, a design method
of diagonal preconditioners for PPDS has been proposed to determine them
automatically from the problem structure. However, the existing method has two
limitations: it requires direct access to all elements of the matrices
representing the linear operators involved in the target optimization problem,
and it is element-wise preconditioning, which makes certain types of proximity
operators impossible to compute analytically. To overcome these limitations, we
establish an Operator-norm-based design method of Variable-wise Diagonal
Preconditioning (OVDP). First, the diagonal preconditioners constructed by OVDP
are defined using only the operator norm or its upper bound of the linear
operator thus eliminating the need for their explicit matrix representations.
Furthermore, since our method is variable-wise preconditioning, it keeps all
proximity operators efficiently computable. We also prove that our
preconditioners satisfy the convergence conditions of PPDS. Finally, we
demonstrate the effectiveness and utility of our method through applications to
hyperspectral image mixed noise removal, hyperspectral unmixing, and graph
signal recovery.Comment: Submitted to IEEE Transactions on Signal Processin
H2TF for Hyperspectral Image Denoising: Where Hierarchical Nonlinear Transform Meets Hierarchical Matrix Factorization
Recently, tensor singular value decomposition (t-SVD) has emerged as a
promising tool for hyperspectral image (HSI) processing. In the t-SVD, there
are two key building blocks: (i) the low-rank enhanced transform and (ii) the
accompanying low-rank characterization of transformed frontal slices. Previous
t-SVD methods mainly focus on the developments of (i), while neglecting the
other important aspect, i.e., the exact characterization of transformed frontal
slices. In this letter, we exploit the potentiality in both building blocks by
leveraging the \underline{\bf H}ierarchical nonlinear transform and the
\underline{\bf H}ierarchical matrix factorization to establish a new
\underline{\bf T}ensor \underline{\bf F}actorization (termed as H2TF). Compared
to shallow counter partners, e.g., low-rank matrix factorization or its convex
surrogates, H2TF can better capture complex structures of transformed frontal
slices due to its hierarchical modeling abilities. We then suggest the
H2TF-based HSI denoising model and develop an alternating direction method of
multipliers-based algorithm to address the resultant model. Extensive
experiments validate the superiority of our method over state-of-the-art HSI
denoising methods
Low-Rank Tensor Recovery with Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization
The nuclear norm and Schatten- quasi-norm of a matrix are popular rank
proxies in low-rank matrix recovery. Unfortunately, computing the nuclear norm
or Schatten- quasi-norm of a tensor is NP-hard, which is a pity for low-rank
tensor completion (LRTC) and tensor robust principal component analysis
(TRPCA). In this paper, we propose a new class of rank regularizers based on
the Euclidean norms of the CP component vectors of a tensor and show that these
regularizers are monotonic transformations of tensor Schatten- quasi-norm.
This connection enables us to minimize the Schatten- quasi-norm in LRTC and
TRPCA implicitly. The methods do not use the singular value decomposition and
hence scale to big tensors. Moreover, the methods are not sensitive to the
choice of initial rank and provide an arbitrarily sharper rank proxy for
low-rank tensor recovery compared to nuclear norm. We provide theoretical
guarantees in terms of recovery error for LRTC and TRPCA, which show relatively
smaller of Schatten- quasi-norm leads to tighter error bounds.
Experiments using LRTC and TRPCA on synthetic data and natural images verify
the effectiveness and superiority of our methods compared to baseline methods
Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022
Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).</p