Hyperspectral image denoising with superpixel segmentation and low-rank representation

Fan, Fan; Ma, Yong; Li, Chang; Mei, Xiaoguang; Huang, Jun; Ma, Jiayi

doi:10.1016/j.ins.2017.02.044

Cited by 121 publications

(72 citation statements)

References 45 publications

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“…For quantitative comparisons, we have used MPSNR and MSSIM, which are simply the PSNR and SSIM values averaged over the spectral bands. We notice that the restoration obtained using our method is better than [44], [45] (source code made public by authors), which is supported by the metrics shown in the figures. The same is visually evident from a comparison of the boxed regions in Figure 15.…”

Section: Hyperspectral Denoisingsupporting

confidence: 78%

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Fast High-Dimensional Bilateral and Nonlocal Means Filtering

Nair

Chaudhury

2019

IEEE Trans. on Image Process.

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Existing fast algorithms for bilateral and nonlocal means filtering mostly work with grayscale images. They cannot easily be extended to high-dimensional data such as color and hyperspectral images, patch-based data, flow-fields, etc. In this paper, we propose a fast algorithm for high-dimensional bilateral and nonlocal means filtering. Unlike existing approaches, where the focus is on approximating the data (using quantization) or the filter kernel (via analytic expansions), we locally approximate the kernel using weighted and shifted copies of a Gaussian, where the weights and shifts are inferred from the data. The algorithm emerging from the proposed approximation essentially involves clustering and fast convolutions, and is easy to implement. Moreover, a variant of our algorithm comes with a guarantee (bound) on the approximation error, which is not enjoyed by existing algorithms. We present some results for high-dimensional bilateral and nonlocal means filtering to demonstrate the speed and accuracy of our proposal. Moreover, we also show that our algorithm can outperform state-of-the-art fast approximations in terms of accuracy and timing.

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Section: Hyperspectral Denoisingsupporting

confidence: 78%

“…(d) [45]. denoising methods [44], [45], where parameters are tuned accordingly. Visual and quantitative comparisons are shown in Figure 15 (Pavia dataset).…”

Section: Hyperspectral Denoisingmentioning

confidence: 99%

Fast High-Dimensional Bilateral and Nonlocal Means Filtering

Nair

Chaudhury

2019

IEEE Trans. on Image Process.

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“…Hazavei [11] applied the hidden Markov tree (HMT) with mixtures of one-sided exponential densities to denoise images. To excavate the spatial information in hyperspectral image, a novel denoising method which integrates superpixel segmentation (SS) into low-rank representation (LRR), is presented in [24]. Because of the advantage in preserving texture in image, total variation is widely applied in the problems of image denoising [25].…”

Section: Image Denoising Methodsmentioning

confidence: 99%

A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter

Liu

Wang

et al. 2017

Applied Sciences

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Abstract:To eliminate the noise of infrared thermal image without reference and noise model, an improved dual-tree complex wavelet transform (DTCWT), optimized by an improved fruit-fly optimization algorithm (IFOA) and bilateral filter (BF), is proposed in this paper. Firstly, the noisy image is transformed by DTCWT, and the noise variance threshold is optimized by the IFOA, which is enhanced through a fly step range with inertia weight. Then, the denoised image will be re-processed using bilateral filter to improve the denoising performance and enhance the edge information. In the experiment, the proposed method is applied to eliminate both addictive noise and multiplicative noise, and the denoising results are compared with other representative methods, such as DTCWT, block-matching and 3D filtering (BM3D), median filter, wiener filter, wavelet decomposition filter (WDF) and bilateral filter. Moreover, the proposed method is applied as pre-processing utilization for infrared thermal images in a coal mining working face.

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“…all the available pixels or just a cropped version of it. [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] In our case, the whole data set of almost 400,000 pixels has been used to show the good scalability of the proposed concept.…”

Section: Image Data Setmentioning

confidence: 99%

When remote sensing meets topological data analysis

Duponchel

2018

J. Spectral Imaging

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Hyperspectral remote sensing plays an increasingly important role in many scientific domains and everyday life problems. Indeed, this imaging concept ends up in applications as varied as catching tax-evaders red-handed by locating new construction and building alterations, searching for aircraft and saving lives after fatal crashes, detecting oil spills for marine life and environmental preservation, spying on enemies with reconnaissance satellites, watching algae grow as an indicator of environmental health, forecasting weather to warn about natural disasters and much more. From an instrumental point of view, we can say that the actual spectrometers have rather good characteristics, even if we can always increase spatial resolution and spectral range. In order to extract ever more information from such experiments and develop new applications, we must, therefore, propose multivariate data analysis tools able to capture the shape of data sets and their specific features. Nevertheless, actual methods often impose a data model which implicitly defines the geometry of the data set. The aim of the paper is thus to introduce the concept of topological data analysis in the framework of remote sensing, making no assumptions about the global shape of the data set, but also allowing the capture of its local features.

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Hyperspectral image denoising with superpixel segmentation and low-rank representation

Cited by 121 publications

References 45 publications

Fast High-Dimensional Bilateral and Nonlocal Means Filtering

Fast High-Dimensional Bilateral and Nonlocal Means Filtering

A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter

When remote sensing meets topological data analysis

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