Multi-Frame Blind Restoration for Image of Space Target With FRC and Branch-Attention

Zhu, Peijian; Xie, Chunzhi; Gao, Zhisheng

doi:10.1109/access.2020.3029356

Cited by 3 publications

(2 citation statements)

References 69 publications

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“…Moreover, deep learning deconvolution is used in astronomy [107][108][109]. In this area, observations are inevitably prone to distortion due to the presence of the atmosphere.…”

Section: Application Of Deep Learning In a Deconvolution Problemmentioning

confidence: 99%

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Makarkin

Bratashov

2021

Micromachines

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In modern digital microscopy, deconvolution methods are widely used to eliminate a number of image defects and increase resolution. In this review, we have divided these methods into classical, deep learning-based, and optimization-based methods. The review describes the major architectures of neural networks, such as convolutional and generative adversarial networks, autoencoders, various forms of recurrent networks, and the attention mechanism used for the deconvolution problem. Special attention is paid to deep learning as the most powerful and flexible modern approach. The review describes the major architectures of neural networks used for the deconvolution problem. We describe the difficulties in their application, such as the discrepancy between the standard loss functions and the visual content and the heterogeneity of the images. Next, we examine how to deal with this by introducing new loss functions, multiscale learning, and prior knowledge of visual content. In conclusion, a review of promising directions and further development of deconvolution methods in microscopy is given.

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“…Moreover, deep learning deconvolution is used in astronomy [107][108][109]. In this area, observations are inevitably prone to distortion due to the presence of the atmosphere.…”

Section: Application Of Deep Learning In a Deconvolution Problemmentioning

confidence: 99%

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Makarkin

Bratashov

2021

Micromachines

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“…Based on this view, the degradation process of atmospheric turbulence can be regarded as a spatially linearly invariant system. As a result, the atmospheric turbulence degradation model can be expressed as follows [7][8][9][10]:…”

Section: Introductionmentioning

confidence: 99%

Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning

Guo

Qing

et al. 2022

Photonics

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Removing space-time varying blur and geometric distortions simultaneously from an image is a challenging task. Recent methods (including physical-based methods or learning-based methods) commonly default the turbulence-degraded operator as a fixed convolution operator. Obviously, the assumption does not hold in practice. According to the situation that the real turbulence distorted operator has double uncertainty in space and time dimensions, this paper reports a novel deep transfer learning (DTL) network framework to address this problem. Concretely, the training process of the proposed approach contains two stages. In the first stage, the GoPro Dataset was used to pre-train the Network D1 and freeze the bottom weight parameters of the model; in the second stage, a small amount of the Hot-Air Dataset was employed for finetuning the last two layers of the network. Furthermore, residual fast Fourier transform with convolution block (Res FFT-Conv Block) was introduced to integrate both low-frequency and high-frequency residual information. Subsequently, extensive experiments were carried out with multiple real-world degraded datasets by implementing the proposed method and four existing state-of-the-art methods. In contrast, the proposed method demonstrates a significant improvement over the four reported methods in terms of alleviating the blur and distortions, as well as improving the visual quality.

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SSIM-based sparse image restoration

Omara

Salem

Elsanadily³

et al. 2022

Journal of King Saud University - Computer and Information Scie

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Multi-Frame Blind Restoration for Image of Space Target With FRC and Branch-Attention

Cited by 3 publications

References 69 publications

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

State-of-the-Art Approaches for Image Deconvolution Problems, including Modern Deep Learning Architectures

Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning

SSIM-based sparse image restoration

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