Retinal image restoration by means of blind deconvolution

Marrugo, Andrés G.; Šorel, Michal; Šroubek, Filip; Millán, Marı́a S.

doi:10.1117/1.3652709

Cited by 45 publications

(57 citation statements)

References 36 publications

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“…Manual focusing is error prone, especially when performed by inexperienced photographers, and may lead to images that require additional restoration or enhancement. 3 The auto-focus feature offered in new retinal cameras is a significant advance that ultimately leads to a more robust imaging system, especially for medical screening purposes. On the other hand, the auto-focus feature still relies on the split-line mechanism, whereas in this work we propose a passive focus measure (FM) completely based on image analysis.…”

Section: Introductionmentioning

confidence: 99%

Anisotropy-based robust focus measure for non-mydriatic retinal imaging

et al. 2012

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Abstract. Non-mydriatic retinal imaging is an important tool for diagnosis and progression assessment of ophthalmic diseases. Because it does not require pharmacological dilation of the patient's pupil, it is essential for screening programs performed by non-medical personnel. A typical camera is equipped with a manual focusing mechanism to compensate for the refractive errors in the eye. However, manual focusing is error prone, especially when performed by inexperienced photographers. In this work, we propose a new and robust focus measure based on a calculation of image anisotropy which, in turn, is evaluated from the directional variance of the normalized discrete cosine transform. Simulation and experimental results demonstrate the effectiveness of the proposed focus measure.

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Section: Introductionmentioning

confidence: 99%

Anisotropy-based robust focus measure for non-mydriatic retinal imaging

et al. 2012

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“…This paper follows Ref. 4 but addresses a more general problem: restoration of retinal images in the presence of an SV PSF. In Ref.…”

Section: Description Of the Methodsmentioning

confidence: 99%

“…In Ref. 4, we showed that the single image blind deconvolution for blurred retinal images does not provide a suitable restoration. Moreover, in images with SV blur, the restoration is even worse.…”

Section: Description Of the Methodsmentioning

confidence: 99%

“…In our previous work, 4 we modeled the blurring of a retinal image by convolution with a unique global PSF. This approximation is valid as long as the PSF changes little throughout the field of view (FOV).…”

Section: Sv Model Of Blurmentioning

confidence: 99%

“…In fact, in Ref. 4, we took advantage of this condition and proposed a blind deconvolution method to restore blurred retinal images acquired with a lapse of time, even in the case where structural changes had occurred in the images. In that work, we detected structural changes, which in turn have clinical relevance, and applied a masking operator so that the images would comply with the considered degradation model.…”

Section: Introductionmentioning

confidence: 99%

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Restoration of retinal images with space-variant blur

et al. 2014

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Abstract. Retinal images are essential clinical resources for the diagnosis of retinopathy and many other ocular diseases. Because of improper acquisition conditions or inherent optical aberrations in the eye, the images are often degraded with blur. In many common cases, the blur varies across the field of view. Most image deblurring algorithms assume a space-invariant blur, which fails in the presence of space-variant (SV) blur. In this work, we propose an innovative strategy for the restoration of retinal images in which we consider the blur to be both unknown and SV. We model the blur by a linear operation interpreted as a convolution with a point-spread function (PSF) that changes with the position in the image. To achieve an artifact-free restoration, we propose a framework for a robust estimation of the SV PSF based on an eye-domain knowledge strategy. The restoration method was tested on artificially and naturally degraded retinal images. The results show an important enhancement, significant enough to leverage the images' clinical use.

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In vivo blind‐deconvolution photoacoustic ophthalmoscopy with total variation regularization

Xie

Gao

et al. 2018

Journal of Biophotonics

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Photoacoustic ophthalmoscopy (PAOM) is capable of noninvasively imaging anatomic and functional information of the retina in living rodents. However, the strong ocular aberration in rodent eyes and limited ultrasonic detection sensitivity affect PAOM's spatial resolution and signal-to-noise ratio (SNR) in in vivo eyes. In this work, we report a computational approach to combine blind deconvolution (BD) algorithm with a regularizing constraint based on total variation (BDTV) for PAOM imaging restoration. We tested the algorithm in retinal and choroidal microvascular images in albino rat eyes. The algorithm improved PAOM's lateral resolution by around 2-fold. Moreover, it enabled the improvement in imaging SNR for both major vessels and capillaries, and realized the well-preserved blood vessels' edges simultaneously, which surpasses conventional Richardson-Lucy BD algorithm. The reported results indicate that the BDTV algorithm potentially facilitate PAOM in extracting retinal pathophysiological information by enhancing in vivo imaging quality without physically modifying PAOM's optical configuration.

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Retinal image restoration by means of blind deconvolution

Cited by 45 publications

References 36 publications

Anisotropy-based robust focus measure for non-mydriatic retinal imaging

Anisotropy-based robust focus measure for non-mydriatic retinal imaging

Restoration of retinal images with space-variant blur

In vivo blind‐deconvolution photoacoustic ophthalmoscopy with total variation regularization

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