Restoration of the infrared image blurred by motion

Wang, Ningming; Wang, Jing; Zhang, Yazhou; Sun, Xianzhong

doi:10.1117/12.2268426

Cited by 3 publications

(2 citation statements)

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“…Oswald-Tranta also committed to obtaining accurate temperature measurements by deblurring infrared images [4]. Wang et al [5] used the iterative Wiener filter to estimate the PSF filter of motion blur in infrared images. e deblurring method based on infrared imaging hardware equipment is more expensive.…”

Section: Introductionmentioning

confidence: 99%

Infrared Image Deblurring Based on Generative Adversarial Networks

Zhao

Wang

et al. 2021

International Journal of Optics

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Blind deblurring of a single infrared image is a challenging computer vision problem. Because the blur is not only caused by the motion of different objects but also by the relative motion and jitter of cameras, there is a change of scene depth. In this work, a method based on the GAN and channel prior discrimination is proposed for infrared image deblurring. Different from the previous work, we combine the traditional blind deblurring method and the blind deblurring method based on the learning method, and uniform and nonuniform blurred images are considered, respectively. By training the proposed model on different datasets, it is proved that the proposed method achieves competitive performance in terms of deblurring quality (objective and subjective).

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Section: Introductionmentioning

confidence: 99%

Infrared Image Deblurring Based on Generative Adversarial Networks

Zhao

Wang

et al. 2021

International Journal of Optics

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“…Velusamy et al [7] developed a deconvolution algorithm for IR astronomy applications when the PSF in known. Wang et al [8] used an iterative Wiener filter to estimate the PSF and then apply it to correct for camera motion blur. Lai et al [9] combined Richardson-Lucy blind deconvolution and total-variation regularized deconvolution and demonstrated results that worked well for low-cost thermal cameras.…”

Section: Introductionmentioning

confidence: 99%

A Unified Method for Digital Super-Resolution and Restoration in Infrared Microscopy Imaging

et al. 2019

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Infrared (IR) imaging systems are known to have a range of sensor and optical limitations that result in degraded imagery. Fixed pattern noise (FPN), resulting from pixel-to-pixel response nonuniformity, is a dominant source of error that manifests in collected imagery through the appearance of temporally and spatially correlated noise patterns that are mixed with each image. Furthermore, finite detector size coupled with imperfect system optics can introduce blurring effects and aliasing, ultimately reducing resolution in acquired images. Here, we propose a unified method to reduce FPN and recover high-frequency image content in IR microscopy images. The proposed method uses regularized nonlocal means to highlight spatial features in the scene while maintaining fine textural image details. We derive an iterative optimization method based upon a gradient descent minimization strategy that applies a Wiener deconvolution in each iteration to estimate the blur artifacts. The method is implemented within an embedded mid-wave IR imaging system for microscopy applications. We demonstrate a reduction in FPN and blurring artifacts, achieving improved image resolution in the reconstructed images that are apparent in recovered details on scene objects.

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FPGA-based infrared image deblurring using angular position of IR detector

Doner

Gökcen

2020

Vis Comput

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Restoration of the infrared image blurred by motion

Cited by 3 publications

References 0 publications

Infrared Image Deblurring Based on Generative Adversarial Networks

Infrared Image Deblurring Based on Generative Adversarial Networks

A Unified Method for Digital Super-Resolution and Restoration in Infrared Microscopy Imaging

FPGA-based infrared image deblurring using angular position of IR detector

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