6 research outputs found
Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors
Deconvolution is a widely used strategy to mitigate the blurring and noisy
degradation of hyperspectral images~(HSI) generated by the acquisition devices.
This issue is usually addressed by solving an ill-posed inverse problem. While
investigating proper image priors can enhance the deconvolution performance, it
is not trivial to handcraft a powerful regularizer and to set the
regularization parameters. To address these issues, in this paper we introduce
a tuning-free Plug-and-Play (PnP) algorithm for HSI deconvolution.
Specifically, we use the alternating direction method of multipliers (ADMM) to
decompose the optimization problem into two iterative sub-problems. A flexible
blind 3D denoising network (B3DDN) is designed to learn deep priors and to
solve the denoising sub-problem with different noise levels. A measure of 3D
residual whiteness is then investigated to adjust the penalty parameters when
solving the quadratic sub-problems, as well as a stopping criterion.
Experimental results on both simulated and real-world data with ground-truth
demonstrate the superiority of the proposed method.Comment: IEEE Trans. Geosci. Remote sens. Manuscript submitted June 30, 202
Robust retrieval of material chemical states in X-ray microspectroscopy
X-ray microspectroscopic techniques are essential for studying morphological
and chemical changes in materials, providing high-resolution structural and
spectroscopic information. However, its practical data analysis for reliably
retrieving the chemical states remains a major obstacle to accelerating the
fundamental understanding of materials in many research fields. In this work,
we propose a novel data formulation model for X-ray microspectroscopy and
develop a dedicated unmixing framework to solve this problem, which is robust
to noise and spectral variability. Moreover, this framework is not limited to
the analysis of two-state material chemistry, making it an effective
alternative to conventional and widely-used methods. In addition, an
alternative directional multiplier method with provable convergence is applied
to obtain the solution efficiently. Our framework can accurately identify and
characterize chemical states in complex and heterogeneous samples, even under
challenging conditions such as low signal-to-noise ratios and overlapping
spectral features. Extensive experimental results on simulated and real
datasets demonstrate its effectiveness and reliability.Comment: 12 page
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
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
Learning spectral-spatial prior via 3DDNCNN for hyperspectral image deconvolution
International audienceHyperspectral image (HSI) deconvolution is an ill-posed problem aiming at recovering sharp images with tens or hundreds of spectral channels from blurred and noisy observations. In order to successfully conduct the deconvolution, proper priors are required to regularize the optimization problem. However, handcrafting a good regularizer may not be trivial and complex regularizers lead to difficulties in solving the optimization problem. In this paper, we use the alternating direction method of multipliers (ADMM) to decompose the optimization problem into iterative subproblems where the prior only appears in a denoising subproblem. Then a 3D denoising convolutional neural network (3DDnCNN) is designed and trained with data for solving this problem. In this way, the hyperspectral image deconvolution is then solved with a framework that integrates the optimization techniques and deep learning. Experimental results demonstrate the superiority of the proposed method with several blurring settings in both quantitative and qualitative comparisons