Hyperspectral imaging, providing abundant spatial and spectral information simultaneously, has attracted a lot of interest in recent years. Unfortunately, due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to various degradations, such as noises (random noise), blurs (Gaussian and uniform blur), and downsampled (both spectral and spatial downsample), each corresponding to the HSI denoising, deblurring, and super-resolution tasks, respectively. Previous HSI restoration methods are designed for one specific task only. Besides, most of them start from the 1-D vector or 2-D matrix models and cannot fully exploit the structurally spectral–spatial correlation in 3-D HSI. To overcome these limitations, in this article, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which nonlocal similarity within spectral–spatial cubic and spectral correlation are simultaneously captured by third-order tensors. Furthermore, to improve the capability and flexibility, we formulate it as a weighted low-rank tensor recovery (WLRTR) model by treating the singular values differently. We demonstrate the reweighed strategy, which has been extensively studied in the matrix, also greatly benefits the tensor modeling. We also consider the stripe noise in HSI as the sparse error by extending WLRTR to robust principal component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed WLRTR models consistently outperform state-of-the-art methods in typical HSI low-level vision tasks, including denoising, destriping, deblurring, and super-resolution.

Weighted Low-Rank Tensor Recovery for Hyperspectral Image Restoration

Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization

Stripe and random noise are two different degradation phenomena that commonly coexist in optical remote sensing images, and they are often modeled as inverse problems. In model-based inverse problems, analysis and synthesis sparse representations (SSRs) are used as regularization terms to obtain approximate solutions due to their respective merits, i.e., the nonzero coefficients in SSR are usually used to describe an image, while the indexes of zeros in analysis sparse representation (ASR) are used to characterize the stripe. Inspired by these merits, we propose a unified variational framework, called a joint analysis and weighted synthesis (JAWS) sparsity model, to simultaneously separate the clean image and the stripe from a single optical remote sensing image. To solve the JAWS sparsity model efficiently, an alternating minimization optimization strategy is first employed to separate it into two subproblems that are used for different tasks. One called as weighted SSR (WSSR) is the main for optical remote sensing image denoising, which can be effectively solved by employing the weighted singular value thresholding operator, while the other called as ASR is the main approach for optical remote sensing image destriping, which is optimized by adopting the split Bregman iteration. By minimizing the two subproblems alternatively, the proposed JAWS sparsity model is efficiently solved. Finally, both quantitative and qualitative results of experiments on synthetic and real-world optical remote sensing images validate that the proposed approach is effective and even better than the state of the arts.

Joint Analysis and Weighted Synthesis Sparsity Priors for Simultaneous Denoising and Destriping Optical Remote Sensing Images

Remote sensing images are often contaminated by varying degrees of stripes, which severely affects the visual quality and subsequent application of the data. Unlike with conventional methods, we achieve the destriping by separating the stripe component based on a full analysis of the various stripe properties. Under an optimization framework, an l
 0
-norm-based regularization is used to characterize the global sparse distribution of the stripes. In addition, difference-based constraints are adopted to describe the local smoothness and discontinuity in the along-stripe and across-stripe directions, respectively. The alternating direction method of multipliers is applied to solve and accelerate the model optimization. Experiments with both simulated and real data demonstrate the effectiveness of the proposed model, in terms of both qualitative and quantitative perspectives.

Stripe Noise Separation and Removal in Remote Sensing Images by Consideration of the Global Sparsity and Local Variational Properties

Multispectral image (MSI) destriping is a challenging topic and has been attracting much research attention in remote sensing area due to its importance in improving the image qualities and subsequent applications. The existing destriping methods mainly focus on matrix-based modeling representation, which fails to fully discover the correlation of the stripe component in both spatial dimensions. In this paper, we propose a novel low-rank tensor decomposition framework based MSI destriping method by decomposing the striped image into the image component and stripe component. Specifically, for the image component, we use the anisotropic spatial unidirectional total variation (TV) and spectral TV regularization to enhance the piecewise smoothness in both spatial and spectral domains. Moreover, for the stripe component, we adopt tensor Tucker decomposition and $\ell _{2,1}$ -norm regularization to model the spatial correlation and group sparsity characteristic among all bands, respectively. An efficient algorithm using the augmented Lagrange multiplier method is designed to solve the proposed optimization model. Experiments under various cases of simulated data and real-world data demonstrate the effectiveness of the proposed model over the existing single-band and MSI destriping methods in terms of the qualitative and quantitative.

Destriping of Multispectral Remote Sensing Image Using Low-Rank Tensor Decomposition

Removal of stripe noise with spatially adaptive unidirectional total variation

Robust destriping of MODIS and hyperspectral data using a hybrid unidirectional total variation model

Thermal infrared imaging possesses the ability to monitor unmanned aerial vehicles (UAVs) in both day and night conditions. However, long-range detection of the infrared UAVs often suffers from small/dim targets, heavy clutter, and noise in the complex background. The conventional local prior-based and the nonlocal prior-based methods commonly have a high false alarm rate and low detection accuracy. In this letter, we propose a model that converts small UAV detection into a problem of predicting the residual image (i.e., background, clutter, and noise). Such novel reformulation allows us to directly learn a mapping from the input infrared image to the residual image. The constructed image-to-image network integrates the global and the local dilated residual convolution blocks into the U-Net, which can capture local and contextual structure information well and fuse the features at different scales both for image reconstruction. Additionally, subpixel convolution is utilized to upscale the image and avoid image distortion during upsampling. Finally, the small UAV target image is obtained by subtracting the residual image from the input infrared image. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art ones in detecting real-world infrared images with heavy clutter and dim targets.

Infrared Small UAV Target Detection Based on Residual Image Prediction via Global and Local Dilated Residual Networks

Hyperspectral images are often unavoidably degraded by blur and noise in the acquisition process, which influences subsequent image processing and analysis. Under the maximum a posteriori framework, this article introduces a hyperspectral image deconvolution method based on spectral-spatial total variation prior and an explicit nonnegative constraint, which can preserve the edge in the spatial domain and maintain the discontinuity along the spectral dimension. Additionally, the proposed method fully exploits the spectral correlation between adjacent bands. The alternating direction method of multipliers (ADMM) is employed to efficiently optimize the proposed spectral-spatial total variation deconvolution model. The reason for this interest is that ADMM decomposes a difficult optimization problem into a sequence of much simpler and easily solved sub-problems. The regularization parameter is updated adaptively to make the proposed algorithm more stable. Numerical experiments are conducted in both si...

Gang Zhou

Papers

Removal of stripe noise with spatially adaptive unidirectional total variation

Robust destriping of MODIS and hyperspectral data using a hybrid unidirectional total variation model

Infrared Small UAV Target Detection Based on Residual Image Prediction via Global and Local Dilated Residual Networks

Hyperspectral Image Deconvolution with a Spectral-Spatial Total Variation Regularization

A destriping algorithm based on TV-Stokes and unidirectional total variation model