Image restoration of the open-loop adaptive optics retinal imaging system based on optical transfer function analysis

A combination of dual tree complex transform and the image processing algorithm of semi-blind deconvolution was proposed to eliminate the factors which make the image worse. Firstly, the retinal image obtained from the adaptive optics system was decomposed with dual tree complex transform into two parts. The low frequency image was processed by the algorithm of semi-blind deconvolution with some constraints. And the optical transfer function was used as initial parameter estimate which was constructed with the residual aberration of image system. The high frequency image was processed by denoising. Finally, the goal image was obtained by combining the processed images. The experimental results show that the retinal image quality is improved with this method, the image objective quality evaluation parameters are increased more than 5 times compared with the original image, and the average power spectrum is improved about 6 times in the spatial frequency range(70~90()-1) of retinal cells, which show that the method can contribute to satisfying the requirement of observation of human retinal image.

A new adaptive optics retinal imaging system is presented that is based on the principles of digital holography and dispenses with the wavefront sensor and wavefront corrective element of the conventional adaptive optics system.

In order to obtain a clear image of the retina of model eye, an adaptive optics system used to correct the wave-front error is introduced in this paper. The spatial light modulator that we use here is a liquid crystal on a silicon device instead of a conversional deformable mirror. A paper with carbon granule is used to simulate the retina of human eye. The pupil size of the model eye is adjustable (3-7 mm). A Shack–Hartman wave-front sensor is used to detect the wave-front aberration. With this construction, a value of peak-to-valley is achieved to be 0.086 λ, where λ is wavelength. The modulation transfer functions before and after corrections are compared. And the resolution of this system after correction (691p/m) is very close to the dirraction limit resolution. The carbon granule on the white paper which has a size of 4.7 μm is seen clearly. The size of the retina cell is between 4 and 10 mu;m. So this system has an ability to image the human eye's retina.

Some computational problems arising in adaptive optics imaging systems

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce adaptive x-ray imaging systems using Monolithic Active Pixel Sensors (MAPS) to create optimal diagnostic images. Initial systems concentrate on mammography and cephalography. The on-chip intelligence available to MAPS technology will allow real-time analysis of data during image acquisition, giving the capability to build a truly adaptive imaging system with the potential to create images with maximum diagnostic information within given dose constraints. In our system, the exposure in each image region is optimized and the beam intensity is a function not only of tissue thickness and attenuation, but also of local physical and statistical parameters found in the image itself. Using a linear array of detectors with on-chip intelligence, the system will perform an on-line analysis of the image during the scan and then will optimize the X-ray intensity in order to obtain the maximum diagnostic information from the region of interest while minimizing exposure of less important, or simply less dense, regions. This paper summarizes the testing of the sensors and their electronics carried out using synchrotron radiation, x-ray sources and optical measurements. The sensors are tiled to form a 1.5D linear array. These have been characterised and appropriate correction techniques formulated to take into account misalignments between individual sensors. Full testing of the mammography and cephalography I-ImaS prototypes is now underway and the system intelligence is constantly being upgraded through iterative testing in order to obtain the optimal algorithms and settings.

A digital adaptive optics line-scanning confocal imaging (DAOLCI) system is proposed by applying digital holographic adaptive optics to a digital form of line-scanning confocal imaging system. In DAOLCI, each line scan is recorded by a digital hologram, which allows access to the complex optical field from one slice of the sample through digital holography. This complex optical field contains both the information of one slice of the sample and the optical aberration of the system, thus allowing us to compensate for the effect of the optical aberration, which can be sensed by a complex guide star hologram. After numerical aberration compensation, the corrected optical fields of a sequence of line scans are stitched into the final corrected confocal image. In DAOLCI, a numerical slit is applied to realize the confocality at the sensor end. The width of this slit can be adjusted to control the image contrast and speckle noise for scattering samples. DAOLCI dispenses with the hardware pieces, such as Shack–Hartmann wavefront sensor and deformable mirror, and the closed-loop feedbacks adopted in the conventional adaptive optics confocal imaging system, thus reducing the optomechanical complexity and cost. Numerical simulations and proof-of-principle experiments are presented that demonstrate the feasibility of this idea.

Aim: The objective of this project was to evaluate high resolution images from an adaptive optics retinal imager through comparisons with standard film-based and standard digital fundus imagers. Methods: A clinical prototype adaptive optics fundus imager (AOFI) was used to collect retinal images from subjects with various forms of retinopathy to determine whether improved visibility into the disease could be provided to the clinician. The AOFI achieves low-order correction of aberrations through a closed-loop wavefront sensor and an adaptive optics system. The remaining high-order aberrations are removed by direct deconvolution using the point spread function (PSF) or by blind deconvolution when the PSF is not available. An ophthalmologist compared the AOFI images with standard fundus images and provided a clinical evaluation of all the modalities and processing techniques. All images were also analyzed using a quantitative image quality index. Results: This system has been tested on three human subjects (one normal and two with retinopathy). In the diabetic patient vascular abnormalities were detected with the AOFI that cannot be resolved with the standard fundus camera. Very small features, such as the fine vascular structures on the optic disc and the individual nerve fiber bundles are easily resolved by the AOFI. Conclusion: This project demonstrated that adaptive optic images have great potential in providing clinically significant detail of anatomical and pathological structures to the ophthalmologist.

High-resolution adaptive optics (AO) imaging of retinal neurons in living eyes holds promise for improved diagnosis and better assessment of treatment outcomes for retinal diseases. By integrating different imaging modalities, such as scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT), AO has enabled microscopic views of different retinal neurons, including recently reported retinal ganglion cells. In this study, we present a novel design of a multimodal adaptive optics imaging system to investigate the microscopic structure of living human retina. The optical system was designed using Zemax ray tracing software. The system performance was evaluated in terms of image quality and beam displacement. Optical performance was predicted to achieve diffraction limited image quality at the retinal plane and beam displacement was predicted to be >7× smaller than the pitch of Shack-Hartmann lenslet at the pupil planes for scan angles over 3.6°×3.6° field of view. The initial human subject images are presented. High quality photoreceptor images were acquired in both AO-SLO channel and AO-OCT channel simultaneously at 3° temporal from the fovea. Individual cones are delineated in AO-SLO image, the corresponding AO-OCT image showed four main reflections from outer retina, namely external limiting membrane, cone inner segment/outer segment junction, cone outer segment tip, and retinal pigment epithelium. The system allows flexibly alternating between AO-SLO and AO-OCT modes, which provides complementary views of retinal cells, and the potential to improve disease diagnosis and treatment.

: The end-to-end performance achieved by an adaptive optical (AO) imaging system is determined by a combination of the residual time-varying phase distortions associated with atmospheric turbulence, and the quasi-static unsensed and uncorrectable aberrations in the optical system itself. Although the effects of these two errors on the time averaged Strehl ratio and the time averaged optical transfer function (OTF) of the AO system are not formally separable, such an approximation is found to be accurate to within a few per cent for a range of representative residual wave-front errors. In these calculations, we have combined static optical system aberrations and time-varying residual phase distortion characteristic of deformable mirror (DM) fitting error, wave-front sensor (WFS) noise, and anisoplanatism. The static aberrations consist of focus errors of varying magnitudes as well as a combination of unsensed and uncorrectable mirror figure errors derived from modeling by the Gemini beta-meter Telescopes project. The overall Strehl ratios and OTF's due to the combined effect of these error sources are well approximated as products of separate factors for the static and time-varying aberrations, as long as the overall Strehl ratio due to both errors is greater than about 0.1. For lower Strehl ratios the products provide lower bounds on the actual values of the Strehl ratio and OTF. The speckle transfer function (STF) is also well approximated by a product of two functions, but only where AO compensation is sufficiently good that speckle imaging techniques are usually not required.

High resolution imaging is desirable for advanced study and clinical management of retinal diseases. However, spatial resolution of retinal imaging has been limited due to available numerical aperture and optical aberration of the ocular optics. This study is to develop and validate virtually structured detection (VSD) to surpass diffraction limit for resolution improvement in in vivo retinal imaging of awake human. A rapid line scanning laser ophthalmoscope (SLO) was constructed for in vivo retinal imaging. A high speed (25,000 kHz) camera was used for recording the two-dimensional (2D) light reflectance profile, corresponding to each focused line illumination. VSD was implemented to the 2D light reflectance profiles for super-resolution reconstruction. Because each 2D light reflectance profile was recorded within 40 μs, the intra-frame blur due to eye movements can be ignored. Digital registration was implemented to further compensate for inter-frame eye movements, before the VSD processing. Based on digital processing, the modulation transfer function (MTF) of the imaging system was derived for objective identification of the cut-off frequency of ocular optics, which is essential for robust VSD processing to ensure reliable super-resolution imaging. Dynamic motility analysis of the super-resolution images was implemented to further enhance the imaging contrast of retinal rod and cone photoreceptors. The VSD based super-resolution SLO significantly improved image quality compared with equivalent wide-field imaging. In vivo observation of individual retinal photoreceptors has been demonstrated unambiguously. Dynamic motility analysis of the super-resolution images enhanced the contrast of retinal rod and cone photoreceptors, and revealed sub-cellular structures in cone photoreceptors. In conjunction with rapid line-scan imaging and digital registration to minimize the effect of eye movements, VSD enabled resolution improvement to observe individual retinal photoreceptors without the involvement of adaptive optics (AO). An objective method has been developed to identify MTF to enable quantitative estimation of the cut-off frequency required for robust VSD processing.

Adaptive optics can be used to mitigate the effects of atmospheric turbulence on imaging systems, but the correction is only partial, and deconvolution is often required to improve the resolution. This results in entire optical/digital systems, which are traditionally designed sequentially, i.e., the adaptive optics system is optimised first, and the restoration algorithms are designed a second time. Studies on optical/digital systems have shown that jointly optimizing the whole system is a better alternative. We propose to extend these co-design strategies to the design of an adaptive optics-assisted imaging system. We derive a simple criterion that takes into account the source properties and the entire optical/ digital system performance. To illustrate its interest, we use it to optimize the wavelength distribution between the wavefront sensor and the imaging camera. In addition, we explore the potential of using multiple imaging channels operating at different wavelengths as a means of making an imaging system robust to turbulence strength and source magnitude variations. Later, any parameter of the optical/digital system, if not the entire system itself, could be optimized this way.

Adaptive optics flood-illuminated imaging technology has been successfully used to correct the wavefront aberration of human eyes to obtain high-resolution retinal images. However, because of the pollution of various types of noise and the degradation caused by residual aberration, the noisy images are not very clear and weak edges are difficult to discern. To reveal the abundant detail hidden by large-scale noise and to enhance low-contrast edges, a hybrid filtering and enhancement method is proposed combining bilateral filtering, coherence diffusion, and edge enhancement. Results show that it is effective to improve the visual quality of retinal cell images.

This paper proposes a simple method to achieve the optical transfer function of a circular pupil wavefront coding system with a separable phase mask in Cartesian coordinates. Based on the stationary phase method, the optical transfer function of the circular pupil system can be easily obtained from the optical transfer function of the rectangular pupil system by modifying the cut-off frequency and the on-axial modulation transfer function. Finally, a system with a cubic phase mask is used as an example to illustrate the way to achieve the optical transfer function of the circular pupil system from the rectangular pupil system.

We discuss an adaptive optical system having a self- organized control channel hierarchy (`adaptive' adaptive imaging system). In this system changes in the quality metric are reflected by corresponding changes to the control algorithm structure. Numerical simulations of an adaptive imaging system with a self-organized control structure demonstrate a property unusual for model-free optimization methods: the algorithm convergence speed did not change, or at times even increased, when the number of control channels N increased.

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.