Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images

Cui, Hao; Jia, Peng; Zhang, Guo; Jiang, Yonghua; Li, Litao; Wang, Jingyin; Hao, Xiaoyan

doi:10.1109/tgrs.2019.2947599

Cited by 18 publications

(5 citation statements)

References 30 publications

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“…Wavelet-Fourier transformbased methods have been presented in order to improve stripe noise removal performance [20], [21]. Hybrid approaches combining statistical matching and filtering techniques have also been proposed [22]- [24]. However, some filtering-based methods often suffer from blurring artifacts due to excessive filtering.…”

Section: A Filtering-based Methodsmentioning

confidence: 99%

“…They can be roughly classified into four categories: filtering-based methods, statistical-based methods, deep learning-based methods and optimization-based methods. Filtering-based methods are efficient and easy to implement, but some results can suffer from blurring artifacts due to excessive filtering [8], [16]- [24]. On the other hand, statistical-based methods quickly remove stripe noise by exploiting the statistical properties of images.…”

Section: Introductionmentioning

confidence: 99%

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ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

Kim,

Han,

Jeong

2023

IEEE Access

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Remote sensing images (RSI) are useful for various tasks such as Earth observation and climate change. However, RSI may suffer from stripe noise due to physical limitations in sensor systems. Therefore, image destriping is essential, since stripe noise may cause serious problems in real applications. In this paper, we shall present a new Alternating Direction method of multipliers (ADMM)-based Optimization Model, called ADOM for stripe noise removal in RSI. First, we formulate an optimization function for finding stripe noise components from the observed image for stripe noise removal, and then optimization process for solving the optimization function in order to extract stripe noise component. In the optimization process, we shall propose a weight-based detection strategy for efficient stripe noise component capture, and an ADMM-based acceleration strategy for fast stripe noise removal. In the weight-based detection strategy, we effectively detect stripe noise similar to the image details by using weighted norm generated by adjusting norm and group norm weights based on the momentum coefficient and residual parameter. In the ADMMbased acceleration strategy, we accelerate optimization process by using two control strategies: evidencebased starting point control and momentum-based step-size control. The former provides a starting point for more accurately finding stripe noise component, and the latter accelerates convergence by using the momentum coefficient while providing optimization stability by exploiting the damping coefficient. Our experimental results show that ADOM achieves better performance for both of simulated and real image data sets compared to the other destriping models.INDEX TERMS Destriping, remote sensing image, alternating direction method of multipliers, optimization.

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Section: A Filtering-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

Kim,

Han,

Jeong

2023

IEEE Access

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“…In addition to microscopic images, stripe artifact is a severe limiting issue in remote sensing systems. Several studies provided solutions for this problem [166,167], which could be also utilized for LSFM images with minor modifications. As an example, Chang et al estimated complex stripes distribution by wavelet-based deep convolutional neural network method for destriping remote sensing systems' images [168].…”

Section: Stripes Suppressionmentioning

confidence: 99%

Light-sheet fluorescent microscopy: fundamentals, developments and applications

Kafian,

Mozaffari-Jovin,

Bagheri

et al. 2023

Phys. Scr.

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Deep volumetric microscopy of live objects plays a critical role in biology and medicine. To this end, development of rapid and non-invasive optical methods for 3-dimensional (3D) imaging is still demanding. In this way, light-sheet fluorescence microscopy (LSFM) has emerged as a volumetric microscopy method having high spatial-temporal resolution for imaging of samples within dimensions from submicron to few centimeters with minimum induced photo damaging. Unique features of LSFM allow for its modification and deployments in various fields including developmental biology, pathology, and microfluidics. Considering the wide spectrum of LSFM users, this tutorial review article explains basic concepts and design considerations of LSFM and provides a detailed analysis of various optical configurations of LSFM. Major developments of LSFM for adoption in both research and clinical applications including tissue imaging, diagnostics, and cytometry are also explained. In addition, various designs of light-sheet fluorescent microscopes for use as a stand-alone microscopy unit and an add-on device are discussed.

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“…In 2009, Münch et al [105] took the direction of the stripe into consideration via the wavelet, and the waveletbased FFT method leading to impressive destriping performance. Hybrid approaches combining statistical matching with the filtering methods have also been presented [106]- [109]. It is worth noting that the filtering-based methods are suitable for regular stripes, such as periodical stripes where the frequency can be easily separated from the image structures.…”

Section: B 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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Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images

Cited by 18 publications

References 30 publications

ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

ADOM: ADMM-Based Optimization Model for Stripe Noise Removal in Remote Sensing Image

Light-sheet fluorescent microscopy: fundamentals, developments and applications

Image Restoration for Remote Sensing: Overview and Toolbox

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