Restoration of Single pixel imaging in atmospheric turbulence by Fourier filter and CGAN

Zhang, Leihong; Ye, Hualong; Wang, Zhaorui; Wang, Kaimin; Zhang, Dawei

doi:10.1007/s00340-021-07596-8

Cited by 14 publications

(2 citation statements)

References 24 publications

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“…Massive efforts were devoted to finding a solution for imaging in various turbulences 16 – 23 There is no denying the fact that it is difficult to use the same methods to simultaneously resolve holographic imaging problems with low-SC scenes and multiple intensities of turbulence. Although low-SC and turbulence may not appear to be correlated at first glance, their influence on computational holography can both be described through the concept of SC.…”

Section: Introductionmentioning

confidence: 99%

Harnessing the magic of light: spatial coherence instructed swin transformer for universal holographic imaging

Tong,

Xu,

et al. 2023

Adv. Photon.

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Holographic imaging poses significant challenges when facing real-time disturbances introduced by dynamic environments. The existing deep-learning methods for holographic imaging often depend solely on the specific condition based on the given data distributions, thus hindering their generalization across multiple scenes. One critical problem is how to guarantee the alignment between any given downstream tasks and pretrained models. We analyze the physical mechanism of image degradation caused by turbulence and innovatively propose a swin transformer-based method, termed train-with-coherence-swin (TWC-Swin) transformer, which uses spatial coherence (SC) as an adaptable physical prior information to precisely align image restoration tasks in the arbitrary turbulent scene. The light-processing system (LPR) we designed enables manipulation of SC and simulation of any turbulence. Qualitative and quantitative evaluations demonstrate that the TWC-Swin method presents superiority over traditional convolution frameworks and realizes image restoration under various turbulences, which suggests its robustness, powerful generalization capabilities, and adaptability to unknown environments. Our research reveals the significance of physical prior information in the optical intersection and provides an effective solution for model-to-tasks alignment schemes, which will help to unlock the full potential of deep learning for all-weather optical imaging across terrestrial, marine, and aerial domains.

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Section: Introductionmentioning

confidence: 99%

Harnessing the magic of light: spatial coherence instructed swin transformer for universal holographic imaging

Tong,

Xu,

et al. 2023

Adv. Photon.

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show abstract

“…Because of the special imaging mechanism, SPI has been used in terahertz imaging [5], X-ray imaging [6], [7], 3D imaging [8], [9], and imaging in the complicated environments [10], [11], [12], [13]. Therein, SPI has high robustness in the complicated environments and can be used to image the object through scattering medium, such as biological samples and atmosphere turbulence.…”

Section: Introductionmentioning

confidence: 99%

Single-Pixel Imaging for Partially Occluded Objects

Wang

2023

IEEE Photonics J.

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DeTurb: Atmospheric Turbulence Mitigation with Deformable 3D Convolutions and 3D Swin Transformers

Zou,

Anantrasirichai

2024

Lecture Notes in Computer Science

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Restoration of Single pixel imaging in atmospheric turbulence by Fourier filter and CGAN

Cited by 14 publications

References 24 publications

Harnessing the magic of light: spatial coherence instructed swin transformer for universal holographic imaging

Harnessing the magic of light: spatial coherence instructed swin transformer for universal holographic imaging

Single-Pixel Imaging for Partially Occluded Objects

DeTurb: Atmospheric Turbulence Mitigation with Deformable 3D Convolutions and 3D Swin Transformers

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