Local Low-Rank and Sparse Representation for Hyperspectral Image Denoising

Ma, Guanqun; Huang, Ting‐Zhu; Huang, Jie; Zheng, Chao-Chao

doi:10.1109/access.2019.2923255

Cited by 15 publications

(5 citation statements)

References 55 publications

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“…The low-rank data are projected back into the original space after denoising. In [81], a local low-rank and sparse representation (Local LRSR) was suggested based on a weighted nuclear norm for HSI denoising in the presence of Gaussian noise. Spectral linear unmixing techniques are also considered as a low-rank HSI denoiser [82].…”

Section: A Conventional Techniquesmentioning

confidence: 99%

Image Restoration for Remote Sensing: Overview and Toolbox

Rasti¹,

Chen²,

Dalsasso³

et al. 2021

Preprint

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This paper is under review in GRSM. 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.

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Section: A Conventional Techniquesmentioning

confidence: 99%

Image Restoration for Remote Sensing: Overview and Toolbox

Rasti¹,

Chen²,

Dalsasso³

et al. 2021

Preprint

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show abstract

“…In recent years, new signal processing algorithms represented by compressed sensing and machine learning algorithms represented by deep learning have also been widely used in the field of image denoising. e works in [17][18][19][20] are based on the theory of compressed sensing and realize image reconstruction and denoising through the method of sparse representation. e works in [21][22][23][24] employ a variety of deep learning models for noise image processing and achieve a good denoising effect.…”

Section: Introductionmentioning

confidence: 99%

Image Denoising Based on Improved Gaussian Mixture Model

Hui

Zheng

2021

Scientific Programming

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An image denoising method is proposed based on the improved Gaussian mixture model to reduce the noises and enhance the image quality. Unlike the traditional image denoising methods, the proposed method models the pixel information in the neighborhood around each pixel in the image. The Gaussian mixture model is employed to measure the similarity between pixels by calculating the L2 norm between the Gaussian mixture models corresponding to the two pixels. The Gaussian mixture model can model the statistical information such as the mean and variance of the pixel information in the image area. The L2 norm between the two Gaussian mixture models represents the difference in the local grayscale intensity and the richness of the details of the pixel information around the two pixels. In this sense, the L2 norm between Gaussian mixture models can more accurately measure the similarity between pixels. The experimental results show that the proposed method can improve the denoising performance of the images while retaining the detailed information of the image.

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“…According to the sparse representation principle of human visual system, an image or a signal can be linearly represented by representation bases that are atoms of the dictionary. In the recent years, many research results have been achieved by applying the sparse representation method to the various applications which include image classification [1]- [3], image denoising [4], [5] and face recognition [6], [7]. The sparse representation based classification (SRC) [6] method has already been used to make robust face recognition successful and has achieved the amazing performance in face recognition.…”

Section: Introductionmentioning

confidence: 99%