Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation

Peng, Jiangjun; Wang, Hailin; Cao, Xiangyong; Liu, Xinling; Rui, Xiangyu; Meng, Deyu

doi:10.1109/tgrs.2022.3229012

Cited by 33 publications

(26 citation statements)

References 65 publications

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“…In this section, both simulated and real image data experiments were undertaken to demonstrate the effectiveness of our method. The results of EPLRR-RSID were compared with following denoising methods: LRTA [44], ANLM3D [45], BM4D [46], TDL [47], ITSReg [48], SNLRSF [49], and RCTV [50].…”

Section: Results and Analysismentioning

confidence: 99%

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

et al. 2023

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The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many important details, especially the edges. In this paper, we propose a new denoising method named EPLRR-RSID, which focuses on edge preservation to improve the image quality of the details. Specifically, we considered the low-rank residues as a combination of useful edges and noisy components. In order to better learn the edge information from the low-rank representation (LRR), we designed multi-level knowledge to further distinguish the edge part and the noise part from the residues. Furthermore, a manifold learning framework was introduced in our proposed model to better obtain the edge information, as it can find the structural similarity of the edge part while suppressing the influence of the non-structural noise part. In this way, not only the low-rank part is better learned, but also the edge part is precisely preserved. Extensive experiments on synthetic and several real remote sensing datasets showed that EPLRR-RSID has superior advantages over the compared state-of-the-art (SOTA) approaches, with the mean edge protect index (MEPI) values reaching at least 0.9 and the best values in the no-reference index BRISQUE, which represents that our method improved the image quality by edge preserving.

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Section: Results and Analysismentioning

confidence: 99%

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

et al. 2023

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“…Minimizing the TV regularizer facilitates neighbor pixels to have similar values, potentially leading to better image quality. It is well known that the total variation minimization problem is often solved by using the fast Fourier transform (FFT) with a computational complexity of O(hwct log(hw)) [42]. However, despite the significant improvement in total variation, the high computational complexity is undesirable.…”

Section: Preliminaries and Motivationsmentioning

confidence: 99%

Fast Thick Cloud Removal for Multi-Temporal Remote Sensing Imagery via Representation Coefficient Total Variation

Xu,

Wang,

Wang

2023

Remote Sensing

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Although thick cloud removal is a complex task, the past decades have witnessed the remarkable development of tensor-completion-based techniques. Nonetheless, they require substantial computational resources and may suffer from checkboard artifacts. This study presents a novel technique to address this challenging task using representation coefficient total variation (RCTV), which imposes a total variation regularizer on decomposed data. The proposed approach enhances cloud removal performance while effectively preserving the textures with high speed. The experimental results confirm the efficiency of our method in restoring image textures, demonstrating its superior performance compared to state-of-the-art techniques.

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“…Therefore, there is still room for further enhancement of denoising performance and runtime along this research vein. The recent work [42] has made some analysis that the RCIs inherit the spatial local smoothness from the original HSI. Differently, in this work, we focus on spatial nonlocal self-similarity-based HSI denoising.…”

Section: B Nsr-related Workmentioning

confidence: 99%

“…We first conduct denoising experiments on simulated datasets with ground truth to quantitatively evaluate the performance of the proposed NS3R method. The following 13 related methods are used for comparison, which can be divided into two categories corresponding to the related works in Section II, i.e., the NSR -unrelated methods, including BM4D [55], LRMR [10], E3DTV [35], CTV [36], TDL [17], LLRT [20], KBR [21], and RCTV [42], and NSR -related methods, including FastHyDe [25], SNLRSF [27], NGmeet [28], GLF [26], and TenSRDe [29]. They are mostly representative of the state-of-the-art for HSI denoising.…”

Section: A Simulated Experimentsmentioning

confidence: 99%

Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation

Wang

Peng

Cao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal denoiser but ignore the understanding why these pseudoimages have a similar spatial structure as the original HSI. In this work, we revisit such vein from the respective of principal component analysis (PCA). Inspired by an alternative sparse PCA, we propose a spectral sparse subspace representation strategy to simultaneously learn low-dimensional spectral subspace and novel RCIs with sparse loadings. It turns out that the resulting RCIs possess a more significant spatial structure due to the adaptive sparse combination of spectral bands. A simple nonlocal low-rank approximation is then employed to further remove the residual noise of the RCIs. Finally, the entire denoised HSI is obtained by inverse spectral sparse PCA. Extensive experiments on the simulated and real HSI datasets show that the proposed nonlocal spectral sparse subspace representation method, dubbed as NS3R, has excellent performance both in denoising effect and running time compared with many other state-of-the-art methods.

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Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation

Cited by 33 publications

References 65 publications

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

Fast Thick Cloud Removal for Multi-Temporal Remote Sensing Imagery via Representation Coefficient Total Variation

Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation

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