Deblurring adaptive optics retinal images using deep convolutional neural networks

Xiao, Fei; Zhao, Junlei; Zhao, Haoxin; Dai, Yun; Zhang, Yudong

doi:10.1364/boe.8.005675

Cited by 31 publications

(32 citation statements)

References 23 publications

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“…Concerning computational imaging and microscopy, take Ref. 80 as an example. Apart from more complex AO retinal imaging process for further modeling and training with the network, more e®ective regularization e®ects for the deep neural network such as dropout and batch normalization can improve the network's performance in deblurring the AO retinal images.…”

Section: Discussionmentioning

confidence: 99%

“…With imperfect wavefront correction, in AO bioimaging residual aberrations inevitably exist and downgrade the imaging quality, 80 making it indispensable for appropriate imaging post-processing. This can be obtained by using image restoration techniques, such as nonblind deconvolution based on PSF measurement 81 or more generalized blind deconvolution algorithms, 82,83 which allow simultaneous recovery of blurred images and PSF distributions.…”

Section: Ai-assisted Ao Bioimaging Postprocessingmentioning

confidence: 99%

“…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. 2).…”

Section: Ai-assisted Ao Bioimaging Postprocessingmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Ai-assisted Ao Bioimaging Postprocessingmentioning

confidence: 99%

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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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“…In [9], the authors used the incremental Wiener filter as a blind deconvolution technique to restore the AOSLO retinal images and corresponding PSFs. However, the disadvantage of the blind deconvolution method is that it often gets trapped in local minima, which makes it hard to find a unique solution, especially when there is only a single-blurred image to be restored [10,11].…”

Section: Introductionmentioning

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.…”

Section: Introductionmentioning

confidence: 99%

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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From Pixels to Information: Artificial Intelligence in Fluorescence Microscopy

Han,

You,

Eom

et al. 2024

Advanced Photonics Research

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This review explores how artificial intelligence (AI) is transforming fluorescence microscopy, providing an overview of its fundamental principles and recent advancements. The roles of AI in improving image quality and introducing new imaging modalities are discussed, offering a comprehensive perspective on these changes. Additionally, a unified framework is introduced for comprehending AI‐driven microscopy methodologies and categorizing them into linear inverse problem‐solving, denoising, and nonlinear prediction. Furthermore, the potential of self‐supervised learning techniques that address the challenges associated with training the networks are explored, utilizing unlabeled microscopy data to enhance data quality and expand imaging capabilities. It is worth noting that while the specific examples and advancements discussed in this review focus on fluorescence microscopy, the general approaches and theories are directly applicable to other optical microscopy methods.

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Deblurring adaptive optics retinal images using deep convolutional neural networks

Cited by 31 publications

References 23 publications

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

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

Quality improvement of adaptive optics retinal images using conditional adversarial networks

From Pixels to Information: Artificial Intelligence in Fluorescence Microscopy

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