Atmospheric turbulence removal using convolutional neural network

Gao, Jing; Anantrasirichai, Nantheera; Bull, David

doi:10.48550/arxiv.1912.11350

Cited by 7 publications

(9 citation statements)

References 13 publications

(26 reference statements)

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“…Additionally, scholars also have recently integrated neural network methodologies with the advancement of machine learning. Most of the existing networks are built based on the convolutional neural network (CNN) [8,9,16,26], as the convolution block has a powerful feature extraction capability. For example, Chen et al employed an end-to-end deep convolutional autoencoder combined with the U-Net model to mitigate the turbulence effect [16].…”

Section: Related Workmentioning

confidence: 99%

“…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%

“…For the image restoration tasks in atmospheric turbulence it is difficult to obtain a massive open dataset for training and testing. Thus, these networks rely on simulated datasets in the fixed convolutional form [8,9,16], which present the same problem as the MAP-based methods.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guo

Qing

et al. 2022

Photonics

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“…However, artefacts in the transition areas between foreground and background regions can remain. Removing atmospheric turbulence based on single image processing is proposed using machine learning in [150]. Deep learning techniques to solve this problem are still in their early stages.…”

Section: Restorationmentioning

confidence: 99%

Artificial Intelligence in the Creative Industries: A Review

Anantrasirichai,

Bull

2020

Preprint

Self Cite

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This paper reviews the current state of the art in Artificial Intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement Learning (DRL). We categorise creative applications into five groups related to how AI technologies are used: i) content creation, ii) information analysis, iii) content enhancement and post production workflows, iv) information extraction and enhancement, and v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, machine learning-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of machine learning in domains with fewer constraints, where AI is the 'creator', remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human centric -where it is designed to augment, rather than replace, human creativity.

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“…For the atmospheric turbulence removal, deep learning is still in the early stage and all proposed methods are based on convolutional neural networks (CNNs). The first deep learning-based method, proposed by Gao at al., [5], follows the assumption that the spatial displacement between frames due to atmospheric turbulence has Gaussian distribution. The state-of-the-art Gaussian denoiser, DnCNN [6], architecture is hence used.…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Network

Anantrasirichai¹

2022

Preprint

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Atmospheric turbulence distorts visual imagery and is always problematic for information interpretation by both human and machine. Most well-developed approaches to remove atmospheric turbulence distortion are model-based. However, these methods require high computation and large memory preventing their feasibility of real-time operation. Deep learning-based approaches have hence gained more attention but currently work efficiently only on static scenes. This paper presents a novel learning-based framework offering short temporal spanning to support dynamic scenes. We exploit complex-valued convolutions as phase information, altered by atmospheric turbulence, is captured better than using ordinary real-valued convolutions. Two concatenated modules are proposed. The first module aims to remove geometric distortions and, if enough memory, the second module is applied to refine micro details of the videos. Experimental results show that our proposed framework efficiently mitigate the atmospheric turbulence distortion and significantly outperforms the existing methods.

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Atmospheric turbulence removal using convolutional neural network

Cited by 7 publications

References 13 publications

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

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

Artificial Intelligence in the Creative Industries: A Review

Atmospheric Turbulence Removal with Complex-Valued Convolutional Neural Network

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