Image Restoration for Remote Sensing: Overview and Toolbox

Rasti, Benhood; Chen, Yi; Dalsasso, Emanuele; Dénis, Loïc; Ghamisi, Pedram

doi:10.48550/arxiv.2107.00557

Cited by 4 publications

(3 citation statements)

References 133 publications

(194 reference statements)

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“…However, they may be degraded by additive white Gaussian noise (AWGN) produced by photo or electronic effects, causing them to have low-level vision, which will cause intelligent systems difficulty in interpreting and analyzing their contents. Assuming an observed RSI f corrupted by AWGN g [5], its degradation process can be easily formulated as [6]…”

Section: Introductionmentioning

confidence: 99%

MD3: Model-Driven Deep Remotely Sensed Image Denoising

et al. 2023

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Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods.

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Section: Introductionmentioning

confidence: 99%

MD3: Model-Driven Deep Remotely Sensed Image Denoising

et al. 2023

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“…HSI denoising is an important task in the area of image processing and remote sensing. Various HSI denoising methods are proposed and an effective toolbox is provided in previous work [9]. Current HSI denoising methods can be categorized into two classes: model-based methods and deep-learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

TRQ3DNet: A 3D Quasi-Recurrent and Transformer Based Network for Hyperspectral Image Denoising

Pang

Cao

2022

Remote Sensing

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We propose a new deep neural network termed TRQ3DNet which combines convolutional neural network (CNN) and transformer for hyperspectral image (HSI) denoising. The network consists of two branches. One is built by 3D quasi-recurrent blocks, including convolution and quasi-recurrent pooling operation. Specifically, the 3D convolution can extract the spatial correlation within a band, and spectral correlation between different bands, while the quasi-recurrent pooling operation is able to exploit global correlation along the spectrum. The other branch is composed of a series of Uformer blocks. The Uformer block uses window-based multi-head self-attention (W-MSA) mechanism and the locally enhanced feed-forward network (LeFF) to exploit the global and local spatial features. To fuse the features extracted by the two branches, we develop a bidirectional integration bridge (BI bridge) for better preserving the image feature information. Experimental results on synthetic and real HSI data show the superiority of our proposed network. For example, in the case of Gaussian noise with sigma 70, the PSNR value of our method significantly increases about 0.8 compared with other state-of-the-art methods.

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“…The high representation ability of HSIs can substantially increase how well perform in computer vision tasks , such as classification [1,2], detection [3], tracking [4] and unmixing [5,6]. However, real-world HSIs are frequently tainted by different noises [7] (such Gaussian noise, stripe noise, and impulse noise) in the acquisition process [8], because of the restrictions of the observing environment and imaging system. These annoying noises limit the performance of all of the above processing tasks.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Spectral-Channel Attention Network for Denoising on Hyperspectral Remote Sensing Image

et al. 2022

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Hyperspectral images (HSIs) are frequently contaminated by different noises (Gaussian noise, stripe noise, deadline noise, impulse noise) in the acquisition process as a result of the observation environment and imaging system limitations, which makes image information lost and difficult to recover. In this paper, we adopt a 3D-based SSCA block neural network of U-Net architecture for remote sensing HSI denoising, named SSCANet (Spatial and Spectral-Channel Attention Network), which is mainly constructed by a so-called SSCA block. By fully considering the characteristics of spatial-domain and spectral-domain of remote sensing HSIs, the SSCA block consists of a spatial attention (SA) block and a spectral-channel attention (SCA) block, in which the SA block is to extract spatial information and enhance spatial representation ability, as well as the SCA block to explore the band-wise relationship within HSIs for preserving spectral information. Compared to earlier 2D convolution, 3D convolution has a powerful spectrum preservation ability, allowing for improved extraction of HSIs characteristics. Experimental results demonstrate that our method holds better-restored results than other compared approaches, both visually and quantitatively.

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Image Restoration for Remote Sensing: Overview and Toolbox

Cited by 4 publications

References 133 publications

MD3: Model-Driven Deep Remotely Sensed Image Denoising

MD3: Model-Driven Deep Remotely Sensed Image Denoising

TRQ3DNet: A 3D Quasi-Recurrent and Transformer Based Network for Hyperspectral Image Denoising

Spatial and Spectral-Channel Attention Network for Denoising on Hyperspectral Remote Sensing Image

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