Learning to Deblur Adaptive Optics Retinal Images

Lazareva, Anfisa; Asad, Muhammad; Slabaugh, Greg

doi:10.1007/978-3-319-59876-5_55

Cited by 5 publications

(9 citation statements)

References 23 publications

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“…To make the network work well, large-scale training data are needed, however, a large amount of well-corrected and uncorrected AO retinal image pairs are difficult to obtain, and the images captured with AO are still affected by noises and residual wavefront aberrations. Therefore, like previous learning-based methods [10,12], our model was also trained on synthetically generated retinal images.…”

Section: Datasetsmentioning

confidence: 99%

“…In [10] and [12], the authors proposed approaches for synthetic AO retinal images generation. Both approaches are comprised of four key steps: (1) create a set of ideal retinal images using the algorithm proposed in [24], (2) generate a set of PSFs to simulate the residual optical aberrations of the eye, (3) convolve the ideal retinal images with PSFs to generate synthetic images, and 4add Gaussian noise to the generated images.…”

Section: Datasetsmentioning

confidence: 99%

“…Recently, the deep learning-based method has been introduced to enhance AO retinal images [10,12]. In [12], the authors employed the random forest to learn the mapping of retinal images onto the space of blur kernels expressed in terms of Zernike coefficients. For an input image, the optimal vector of the Zernike coefficient is predicted by the trained random forest, and then the corresponding PSF is reconstructed.…”

Section: Introductionmentioning

confidence: 99%

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Quality improvement of adaptive optics retinal images using conditional adversarial networks

Liu

He³

et al. 2020

Biomed. Opt. Express

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The adaptive optics (AO) technique is widely used to compensate for ocular aberrations and improve imaging resolution. However, when affected by intraocular scatter, speckle noise, and other factors, the quality of the retinal image will be degraded. To effectively improve the image quality without increasing the imaging system's complexity, the post-processing method of image deblurring is adopted. In this study, we proposed a conditional adversarial network-based method for directly learning an end-to-end mapping between blurry and restored AO retinal images. The proposed model was validated on synthetically generated AO retinal images and real retinal images. The restoration results of synthetic images were evaluated with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), perceptual distance, and error rate of cone counting. Moreover, the blind image quality index (BIQI) was used as the no-reference image quality assessment (NR-IQA) algorithm to evaluate the restoration results on real AO retinal images. The experimental results indicate that the images restored by the proposed method have sharper quality and higher signal-to-noise ratio (SNR) when compared with other state-of-the-art methods, which has great practical significance for clinical research and analysis.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quality improvement of adaptive optics retinal images using conditional adversarial networks

Liu

He³

et al. 2020

Biomed. Opt. Express

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show abstract

“…For example, to deblur AO retinal images, a method using the deconvolution method and Random Forest was proposed in 2017 to learn the mapping of retinal images onto the space of blur kernels. 84 The reconstruction performance of this method, however, is limited due to the dependency of the system specicity on a nonblind deconvolution algorithm. More recently, deep learning has been proposed to restore the degraded AO retinal images 80 (illustrated in Fig.…”

Section: Ai-assisted Ao Bioimaging Postprocessingmentioning

confidence: 99%

Artificial intelligence-assisted light control and computational imaging through scattering media

Cheng

Luo

et al. 2019

J. Innov. Opt. Health Sci.

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Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around 2007. Wavefront shaping is aimed at overcoming the strong scattering, featured by random interference, namely speckle patterns. This randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber, yet this randomness is actually deterministic and potentially can be time reversal or precompensated. Various wavefront shaping approaches, such as optical phase conjugation, iterative optimization, and transmission matrix measurement, have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering medium. The performance of these modulations, however, is far from satisfaction. Most recently, artificial intelligence has brought new inspirations to this field, providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links them. In this paper, we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning (deep learning in particular) for further advancements in the field.

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“…However, this blind deconvolution algorithm has a drawback of getting trapped in local minima that makes it hard to find a unique solution, especially when there is only a single blurred image to be restored [5]. Recently, a learning-based blind deconvolution method has been introduced to deblur AO retinal images [10]. This method uses a Random Forest to learn the mapping of retinal images onto the space of blur kernels expressed in terms of Zernike coefficients.…”

Section: Introductionmentioning

confidence: 99%

Deblurring adaptive optics retinal images using deep convolutional neural networks

Xiao

Zhao

et al. 2017

Biomed. Opt. Express

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Abstract:The adaptive optics (AO) can be used to compensate for ocular aberrations to achieve near diffraction limited high-resolution retinal images. However, many factors such as the limited aberration measurement and correction accuracy with AO, intraocular scatter, imaging noise and so on will degrade the quality of retinal images. Image post processing is an indispensable and economical method to make up for the limitation of AO retinal imaging procedure. In this paper, we proposed a deep learning method to restore the degraded retinal images for the first time. The method directly learned an end-to-end mapping between the blurred and restored retinal images. The mapping was represented as a deep convolutional neural network that was trained to output high-quality images directly from blurry inputs without any preprocessing. This network was validated on synthetically generated retinal images as well as real AO retinal images. The assessment of the restored retinal images demonstrated that the image quality had been significantly improved.

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Learning to Deblur Adaptive Optics Retinal Images

Cited by 5 publications

References 23 publications

Quality improvement of adaptive optics retinal images using conditional adversarial networks

Quality improvement of adaptive optics retinal images using conditional adversarial networks

Artificial intelligence-assisted light control and computational imaging through scattering media

Deblurring adaptive optics retinal images using deep convolutional neural networks

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