The total variation (TV) regularizer is a widely used technique in image processing tasks to model an image’s local smoothness property. Intrinsically, the TV regularizer imposes sparsity constraints on the gradient maps of the image, which inevitably weakens the image texture structure and thus affects the quality of image restoration. To alleviate this issue, we propose a novel texture-preserved total variation (TPTV) regularizer for hyperspectral image (HSI) by introducing a weighting scheme. Specifically, the weights are assigned to the gradient maps of HSI, which help slack the sparsity constraint for the pixels with large variations, thus preserving the texture structure. Additionally, we elaborate an empirical method to learn the weights adaptively from observed HSI. Then, we propose an HSI denoising method based on the TPTV regularizer. Experimental results on synthetic and real HSI illustrate the superiority of our proposed method over other state-of-the-art methods. In addition, the proposed weighting scheme can be finely embedded into other TV regularizers and protect the image texture. The experiment results also demonstrate that the denoising performance of the original method is significantly improved after embedding the weighting scheme.

Recently, deep learning techniques have been extensively studied for pansharpening, which aims to generate a high resolution multispectral (HRMS) image by fusing a low resolution multispectral (LRMS) image with a high resolution panchromatic (PAN) image. However, existing deep learning-based pansharpening methods directly learn the mapping from LRMS and PAN to HRMS. These network architectures always lack sufficient interpretability, which limits further performance improvements. To alleviate this issue, we propose a novel deep network for pansharpening by combining the model-based methodology with the deep learning method. Firstly, we build an observation model for pansharpening using the convolutional sparse coding (CSC) technique and design a proximal gradient algorithm to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as Proximal PanNet, by learning the proximal operators using convolutional neural networks. Finally, all the learnable modules can be automatically learned in an end-to-end manner. Experimental results on some benchmark datasets show that our network performs better than other advanced methods both quantitatively and qualitatively.

Facing with rapidly increasing demands for analyzing high-order data or multiway data, feature-extracting methods become imperative for analysis and processing. The traditional feature-extracting methods, however, either need to overly vectorize the data and smash the original structure hidden in data, such as PCA and PCA-like methods, which is unfavorable to the data recovery, or cannot eliminate the redundant information very well, such as tucker decomposition (TD) and TD-like methods. To overcome these limitations, we propose a more flexible and more powerful tool, called the multiview principal components analysis (Multiview-PCA) in this article. By segmenting a random tensor into equal-sized subarrays called sections and maximizing variations caused by orthogonal projections of these sections, the Multiview-PCA finds principal components in a parsimonious and flexible way. In so doing, two new operations on tensors, the S -direction inner/outer product, are introduced to formulate tensor projection and recovery. With different segmentation ways characterized by section depth and direction, the Multiview-PCA can be implemented many times in different ways, which defines the sequential and global Multiview-PCA, respectively. These multiple Multiview-PCA take the PCA and PCA-like, and TD and TD-like as the special cases, which correspond to the deepest section depth and the shallowest section depth, respectively. We propose an adaptive depth and direction selection algorithm for the implementation of Multiview-PCA. The Multiview-PCA is then tested in terms of subspace recovery ability, compression ability, and feature extraction performance when applied to a set of artificial data, surveillance videos, and hyperspectral imaging data. All numerical results support the flexibility, effectiveness, and usefulness of Multiview-PCA.

Hyperspectral image (HSI) is always corrupted by various types of noise during image capturing, such as Gaussian noise, stripe noise, deadline noise, impulse noise, and more. Such complicated noise significantly degrades imaging quality and thus limits the performance of downstream vision tasks. Current HSI denoising methods tackle this problem by modeling either the spectral-spatial prior of HSI or the noise characteristic of HSI, and few work consider the two aspects simultaneously. In this paper, we propose a new HSI denoising method by simultaneously modeling the HSI prior and the HSI noise characteristic. Specifically, we firstly utilize the non independent and identically distributed (non i.i.d.) mixture of Gaussian (MoG) assumption to characterize the complex noise, which corresponds to optimize a weighted fidelity function. Secondly, we exploit HSI’s non-local similarity and spatial-spectral correlation priors by applying non-local low rank model. Thirdly, we design an adaptive edge preserving total variation regularization term to characterize the non-local smooth property of HSI. Finally, we propose a new denoising model and develop effective ADMM algorithm to solve it. Extensive experiments on simulated data and real data substantiate the superiority of the proposed method beyond state-of-the-arts.

Hyperspectral image (HSI) denoising has been attracting much research attention in remote sensing area due to its importance in improving the HSI qualities. The existing HSI denoising methods mainly focus on specific spectral and spatial prior knowledge in HSIs, and share a common underlying assumption that the embedded noise in HSI is independent and identically distributed (i.i.d.). In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i.i.d. statistical structures and the under-estimation to this noise complexity often tends to evidently degenerate the robustness of current methods. To alleviate this issue, this paper attempts the first effort to model the HSI noise using a non-i.i.d. mixture of Gaussians (NMoGs) noise assumption, which finely accords with the noise characteristics possessed by a natural HSI and thus is capable of adapting various practical noise shapes. Then we integrate such noise modeling strategy into the low-rank matrix factorization (LRMF) model and propose an NMoG-LRMF model in the Bayesian framework. A variational Bayes algorithm is then designed to infer the posterior of the proposed model. As substantiated by our experiments implemented on synthetic and real noisy HSIs, the proposed method performs more robust beyond the state-of-the-arts.

Many computer vision problems can be posed as learning a low-dimensional subspace from high-dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problems using L1-norm and L2-norm losses, which mainly deal with the Laplace and Gaussian noises, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as mixture of exponential power (MoEP) distributions and then proposes a penalized MoEP (PMoEP) model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture distribution is adapted from a series of preliminary superor sub-Gaussian candidates. Moreover, by facilitating the local continuity of noise components, we embed Markov random field into the PMoEP model and then propose the PMoEP-MRF model. A generalized expectation maximization (GEM) algorithm and a variational GEM algorithm are designed to infer all parameters involved in the proposed PMoEP and the PMoEPMRF model, respectively. The superiority of our methods is demonstrated by extensive experiments on synthetic data, face modeling, hyperspectral image denoising, and background subtraction.

Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data. The low rank matrix factorization (LRMF) represents a commonly utilized subspace learning strategy. Most of the current LRMF techniques are constructed on the optimization problem using L_1 norm and L_2 norm, which mainly deal with Laplacian and Gaussian noise, respectively. To make LRMF capable of adapting more complex noise, this paper proposes a new LRMF model by assuming noise as Mixture of Exponential Power (MoEP) distributions and proposes a penalized MoEP model by combining the penalized likelihood method with MoEP distributions. Such setting facilitates the learned LRMF model capable of automatically fitting the real noise through MoEP distributions. Each component in this mixture is adapted from a series of preliminary super-or sub-Gaussian candidates. An Expectation Maximization (EM) algorithm is also designed to infer the parameters involved in the proposed PMoEP model. The advantage of our method is demonstrated by extensive experiments on synthetic data, face modeling and hyperspectral image restoration.