Deblurring and Sparse Unmixing for Hyperspectral Images

Image super-resolution has wide applications in biomedical imaging, computer vision, image recognition, etc. In this paper, we present a fast single-image super-resolution method based on deconvolution strategy. The deconvolution process is implemented via a fast total variation deconvolution (FTVd) method that runs very fast. In particular, due to the inaccuracy of kernel, we utilize an iterative strategy to correct the kernel. The experimental results show that the proposed method can improve image resolution effectively and pick up more image structures. In addition, the speed of the proposed method is fast.

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“…Actually, similar cases with the above problems have been studied by many works [34][35][36][37][38][39]. In particular, FTVd method [34] …”

Section: Image Super-resolution Problemmentioning

confidence: 94%

Super-resolution via a fast deconvolution with kernel estimation

Han

Huang

Deng

et al. 2016

J Image Video Proc.

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“…Recently, anisotropic TV based methods have been used to remote sensing images processing and achieved comparable results, including hyperspectral images restoration [2,4,5], and sparse unmixing [8,9]. Thus, to remove stripe noise and random noise from the observed image, we use anisotropic TV regularization which has a wide array of applications in digital imaging as well as preserving sharp edges to recover the clean image.…”

Section: Tv Regularizationmentioning

confidence: 99%

“…Recently, many different denosing methods which mainly aim at random noise have been proposed for restoration of remote sensing images [2][3][4][5][6]. However, many images are badly degraded by stripe noise, and the stripe noise in remote sensing images not only greatly degrades the image quality, but also results in low accuracy in classification [7], sparse unmixing [8][9][10], object segmentation [11], and target detection [12]. Therefore, destriping also has became an essential and inevitable issue before the subsequent analysis and applications of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

et al. 2017

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Remote sensing images have been used in many fields, such as urban planning, military, and environment monitoring, but corruption by stripe noise limits its subsequent applications. Most existing stripe noise removal (destriping) methods aim to directly estimate the clear images from the stripe images without considering the intrinsic properties of stripe noise, which causes the image structure destroyed. In this paper, we propose a new destriping method from the perspective of image decomposition, which takes the intrinsic properties of stripe noise and image characteristics into full consideration. The proposed method integrates the unidirectional total variation (TV) regularization, group sparsity regularization, and TV regularization together in an image decomposition framework. The first two terms are utilized to exploit the stripe noise properties by implementing statistical analysis, and the TV regularization is adopted to explore the spatial piecewise smooth structure of stripe-free image. Moreover, an efficient alternating minimization scheme is designed to solve the proposed model. Extensive experiments on simulated and real data demonstrate that our method outperforms several existing state-of-the-art destriping methods in terms of both quantitative and qualitative assessments.

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“…In [5], Iordache et al proposed the SUnSAL-TV method, which utilizes the spatial information of the first-order pixel neighborhood system of the abundance map through total variation (TV) on sparse unmixing formulation. In [25], Zhao et al studied total variation regularization in deblurring and sparse unmixing of hyperspectral images and their method gets better performance than SUnSAL-TV. Taking the non-local spatial information of whole abundance image into account, the non-local mean unmixing method which utilizes the similar patterns and structures in the abundance map is proposed in [26].…”

Section: Introductionmentioning

confidence: 99%

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

et al. 2018

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Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images. Plenty of sparse unmixing methods have been proposed in the literature that achieved promising performance. However, many of these methods overlook the latent geometrical structure of the hyperspectral data which limit their performance to some extent. To address this issue, a double reweighted sparse and graph regularized unmixing method is proposed in this paper. Specifically, a graph regularizer is employed to capture the correlation information between abundance vectors, which makes use of the property that similar pixels in a spectral neighborhood have higher probability to share similar abundances. In this way, the latent geometrical structure of the hyperspectral data can be transferred to the abundance space. In addition, a double weighted sparse regularizer is used to enhance the sparsity of endmembers and the fractional abundance maps, where one weight is introduced to promote the sparsity of endmembers as a hyperspectral image typically contains fewer endmembers compared to the overcomplete spectral library and the other weight is exploited to improve the sparsity of the abundance matrix. The weights of the double weighted sparse regularizer used for the next iteration are adaptively computed from the current abundance matrix. The experimental results on synthetic and real hyperspectral data demonstrate the superiority of our method compared with some state-of-the-art approaches.

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Deblurring and Sparse Unmixing for Hyperspectral Images

Cited by 154 publications

References 30 publications

Super-resolution via a fast deconvolution with kernel estimation

Super-resolution via a fast deconvolution with kernel estimation

Stripe noise removal of remote sensing images by total variation regularization and group sparsity constraint

Double Reweighted Sparse Regression and Graph Regularization for Hyperspectral Unmixing

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