Zero-Shot Hyperspectral Image Denoising With Separable Image Prior

Imamura, Ryuji; Itasaka, Tatsuki; Okuda, Masahiro

doi:10.1109/iccvw.2019.00178

Cited by 39 publications

(17 citation statements)

References 37 publications

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“…DSConv can extract local delicate features of the image by considering the information of the position and channel separately. Imamura et al designed a denoising network for hyperspectral images using DSConv and demonstrated its ability to realize efficient restoration [ 46 ]. The advantage of DSConv is that it reduces the number of network parameters and the computational complexity in convolution operations [ 42 – 44 ].…”

Section: Methodsmentioning

confidence: 99%

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Liu

2021

Computational Intelligence and Neuroscience

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Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D-Parallel-RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground-truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1-weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D-Parallel-RicianNet network achieves performance superior to that of several state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation.

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Section: Methodsmentioning

confidence: 99%

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Liu

2021

Computational Intelligence and Neuroscience

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“…In recent works, it is observed that learning-based techniques are utilized for noise reduction in HSI data. Some notable contributions include [25], [26], [27], [28], [29] and [30]. Authors in [25] employ a cubic noisy-clean image learning approach where non-linearity functions are also trainable along with convolutional weights and biases.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by the pioneering work of Deep Image Prior (DIP); noisy HSI data is recovered in [27] using the assumption of prior introduced by untrained neural network and early stopping as the regularization strategy. A self-supervised learning approach using separable image prior is explored in [26]. However, since most of these models [25]- [29] are not trained over mixed noise in corrupted HSI data; they yields sub-optimal results when these techniques are tested over real HSI data.…”

Section: Related Workmentioning

confidence: 99%

Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework

Aetesam¹,

Maji

Yahia

2021

IEEE Access

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Hyperspectral images are corrupted by a combination of Gaussian-impulse noise. On one hand, the traditional approach of handling the denoising problem using maximum a posteriori criterion is often restricted by the time-consuming iterative optimization process and design of hand-crafted priors to obtain an optimal result. On the other hand, the discriminative learning-based approaches offer fast inference speed over a trained model; but are highly sensitive to the noise level used for training. A discriminative model trained with a loss function which does not accord with the Bayesian degradation process often leads to sub-optimal results. In this paper, we design the training paradigm emphasizing the role of loss functions; similar to as observed in model-based optimization methods. As a result; loss functions derived in Bayesian setting and employed in neural network training boosts the denoising performance. Extensive analysis and experimental results on synthetically corrupted and real hyperspectral dataset suggest the potential applicability of the proposed technique under a wide range of homogeneous and heterogeneous noisy settings.

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“…Self-supervised techniques also rely on the observed image for training while they create their own training sets from the observed noisy image to train the deep network. In [90], a self-supervised (Zero-shot) denoising technique was proposed for HSIs in which CNN was trained based on the observed image, which is assumed to be the target, and an input image, which is obtained by adding noise to the observed image. Therefore, the network can be only trained using the observed image.…”

Section: B Deep Learning-based 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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Zero-Shot Hyperspectral Image Denoising With Separable Image Prior

Cited by 39 publications

References 37 publications

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Bayesian Approach in a Learning-Based Hyperspectral Image Denoising Framework

Image Restoration for Remote Sensing: Overview and Toolbox

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