Superresolution in turbulent videos: making profit from damage

Yaroslavsky, Leonid P.; Fishbain, Barak; Shabat, Gil; Ideses, Ianir A.

doi:10.1364/ol.32.003038

Cited by 21 publications

(13 citation statements)

References 7 publications

(9 reference statements)

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“…Applications of these approaches to imitation of image retrieval through turbulent media (an illustration one can see at [ 120]) and to stabilization and superresolution of turbulent videos are presented in Refs. [ 121], [ 122] and [ 123].…”

Section: Image Resampling and Building "Continuous" Image Modelsmentioning

confidence: 99%

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Yaroslavsky¹

2015

J. Opt.

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Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet and alike. They are the basic tool in image compression, in image restoration, in image resampling and geometrical transformations and can be traced back to early 1970-ths. The paper presents a review of these methods with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive transform domain filters for image restoration, to methods of precise image resampling and image reconstruction from sparse samples and up to "compressive sensing" approach that has gained popularity in last few years. The review has a tutorial character and purpose.

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Section: Image Resampling and Building "Continuous" Image Modelsmentioning

confidence: 99%

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Yaroslavsky¹

2015

J. Opt.

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“…As a result, the image sampling grid defined by the video camera sensor may be considered to be chaotically moving over a stationary image scene. This phenomenon allows for the generation of images with larger number of samples than those provided by the camera if image frames are combined with appropriate resampling [2,[60][61][62][63]. Generally, such a super-resolution process consists of two main stages [2,[64][65][66][67][68].…”

Section: Generation Of Super-resolved Stabilized Output Framesmentioning

confidence: 99%

“…Visual-range long-distance observations are usually affected by atmospheric turbulence, which causes spatial and temporal fluctuations to the index of refraction of the atmosphere [1], resulting in chaotic geometrical distortions. On the other hand, thermal channels are less vulnerable to the turbulent effects [2][3][4][5][6][7] but usually suffer from substantial sensor noise and reduced resolution as compared to their visual-range counterparts [8]. One way to overcome those problems is to apply data fusion techniques.…”

Section: Introductionmentioning

confidence: 99%

Spatial, Temporal, and Interchannel Image Data Fusion for Long‐Distance Terrestrial Observation Systems

Fishbain

Yaroslavsky

Ideses

2008

Advances in Optical Technologies

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This paper presents methods for intrachannel and interchannel fusion of thermal and visual sensors used in long-distance terrestrial observation systems. Intrachannel spatial and temporal fusion mechanisms used for image stabilization, super-resolution, denoising, and deblurring are supplemented by interchannel data fusion of visual-and thermal-range channels for generating fused videos intended for visual analysis by a human operator. Tests on synthetic, as well as on real-life, video sequences have confirmed the potential of the suggested methods.

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“…This joint restoration problem has received more limited attention in the literature than SR and TM alone. [10][11][12][13][14][15] It is interesting to compare and contrast how SR and TM methods exploit multiple frames for restoration. In the case of SR, spatial sampling diversity is provided by the multiple input frames.…”

Section: Introductionmentioning

confidence: 99%

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

et al. 2019

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An extension of the fusion of interpolated frames superresolution (FIF SR) method to perform SR in the presence of atmospheric optical turbulence is presented. The goal of such processing is to improve the performance of imaging systems impacted by turbulence. We provide an optical transfer function analysis that illustrates regimes where significant degradation from both aliasing and turbulence may be present in imaging systems. This analysis demonstrates the potential need for simultaneous SR and turbulence mitigation (TM). While the FIF SR method was not originally proposed to address this joint restoration problem, we believe it is well suited for this task. We propose a variation of the FIF SR method that has a fusion parameter that allows it to transition from traditional diffraction-limited SR to pure TM with no SR as well as a continuum in between. This fusion parameter balances subpixel resolution, needed for SR, with the amount of temporal averaging, needed for TM and noise reduction. In addition, we develop a model of the interpolation blurring that results from the fusion process, as a function of this tuning parameter. The blurring model is then incorporated into the overall degradation model that is addressed in the restoration step of the FIF SR method. This innovation benefits the FIF SR method in all applications. We present a number of experimental results to demonstrate the efficacy of the FIF SR method in different levels of turbulence. Simulated imagery with known ground truth is used for a detailed quantitative analysis. Three real infrared image sequences are also used. Two of these include bar targets that allow for a quantitative resolution enhancement assessment.

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Superresolution in turbulent videos: making profit from damage

Cited by 21 publications

References 7 publications

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Spatial, Temporal, and Interchannel Image Data Fusion for Long‐Distance Terrestrial Observation Systems

Fusion of interpolated frames superresolution in the presence of atmospheric optical turbulence

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