Eyeglasses-free display: towards correcting visual aberrations with computational light field displays

Millions of people worldwide need glasses or contact lenses to see or read properly. We introduce a computational display technology that predistorts the presented content for an observer, so that the target image is perceived without the need for eyewear. By designing optics in concert with prefiltering algorithms, the proposed display architecture achieves significantly higher resolution and contrast than prior approaches to vision-correcting image display. We demonstrate that inexpensive light field displays driven by efficient implementations of 4D prefiltering algorithms can produce the desired vision-corrected imagery, even for higher-order aberrations that are difficult to be corrected with glasses. The proposed computational display architecture is evaluated in simulation and with a low-cost prototype device.National Science Foundation (U.S.) (grant number IIS-1219241)National Science Foundation (U.S.) (grant number IIS-1116718)Natural Sciences and Engineering Research Council of Canada (NSERC Postdoctoral Fellowship

Eyeglasses-free display: towards correcting visual aberrations with computational light field displays

Millions of people worldwide need glasses or contact lenses to see or read properly. We introduce a computational display technology that predistorts the presented content for an observer, so that the target image is perceived without the need for eyewear. By designing optics in concert with prefiltering algorithms, the proposed display architecture achieves significantly higher resolution and contrast than prior approaches to vision-correcting image display. We demonstrate that inexpensive light field displays driven by efficient implementations of 4D prefiltering algorithms can produce the desired vision-corrected imagery, even for higher-order aberrations that are difficult to be corrected with glasses. The proposed computational display architecture is evaluated in simulation and with a low-cost prototype device.National Science Foundation (U.S.) (grant number IIS-1219241)National Science Foundation (U.S.) (grant number IIS-1116718)Natural Sciences and Engineering Research Council of Canada (NSERC Postdoctoral Fellowship

Fast Two-step Blind Optical Aberration Correction

The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion. This degradation is characterized by the point-spread function (PSF), which depends on the wavelengths of light and is variable across the imaging field. In this paper, we propose a two-step scheme to correct optical aberrations in a single raw or JPEG image, i.e., without any prior information on the camera or lens. First, we estimate local Gaussian blur kernels for overlapping patches and sharpen them with a non-blind deblurring technique. Based on the measurements of the PSFs of dozens of lenses, these blur kernels are modeled as RGB Gaussians defined by seven parameters. Second, we remove the remaining lateral chromatic aberrations (not contemplated in the first step) with a convolutional neural network, trained to minimize the red/green and blue/green residual images. Experiments on both synthetic and real images show that the combination of these two stages yields a fast state-of-the-art blind optical aberration compensation technique that competes with commercial non-blind algorithms.Comment: 28 pages, 20 figures, accepted at ECCV'22 as a poste

Full-field calibration of color camera chromatic aberration using absolute phase maps

The refractive index of a lens varies for different wavelengths of light, and thus the same incident light with different wavelengths has different outgoing light. This characteristic of lenses causes images captured by a color camera to display chromatic aberration (CA), which seriously reduces image quality. Based on an analysis of the distribution of CA, a full-field calibration method based on absolute phase maps is proposed in this paper. Red, green, and blue closed sinusoidal fringe patterns are generated, consecutively displayed on an LCD (liquid crystal display), and captured by a color camera from the front viewpoint. The phase information of each color fringe is obtained using a four-step phase-shifting algorithm and optimum fringe number selection method. CA causes the unwrapped phase of the three channels to differ. These pixel deviations can be computed by comparing the unwrapped phase data of the red, blue, and green channels in polar coordinates. CA calibration is accomplished in Cartesian coordinates. The systematic errors introduced by the LCD are analyzed and corrected. Simulated results show the validity of the proposed method and experimental results demonstrate that the proposed full-field calibration method based on absolute phase maps will be useful for practical software-based CA calibration

Tailored displays to compensate for visual aberrations

We introduce tailored displays that enhance visual acuity by decomposing virtual objects and placing the resulting anisotropic pieces into the subject's focal range. The goal is to free the viewer from needing wearable optical corrections when looking at displays. Our tailoring process uses aberration and scattering maps to account for refractive errors and cataracts. It splits an object's light field into multiple instances that are each in-focus for a given eye sub-aperture. Their integration onto the retina leads to a quality improvement of perceived images when observing the display with naked eyes. The use of multiple depths to render each point of focus on the retina creates multi-focus, multi-depth displays. User evaluations and validation with modified camera optics are performed. We propose tailored displays for daily tasks where using eyeglasses are unfeasible or inconvenient (e.g., on head-mounted displays, e-readers, as well as for games); when a multi-focus function is required but undoable (e.g., driving for farsighted individuals, checking a portable device while doing physical activities); or for correcting the visual distortions produced by high-order aberrations that eyeglasses are not able to.Conselho Nacional de Pesquisas (Brazil) (CNPq-Brazil fellowship 142563/2008-0)Conselho Nacional de Pesquisas (Brazil) (CNPq-Brazil fellowship 308936/2010-8)Conselho Nacional de Pesquisas (Brazil) (CNPq-Brazil fellowship 480485/2010- 0)National Science Foundation (U.S.) (NSF CNS 0913875)Alfred P. Sloan Foundation (fellowship)United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award)Massachusetts Institute of Technology. Media Laboratory (Consortium Members

Dynamic Image Precompensation for Improving Visual Performance of Computer Users with Ocular Aberrations

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user\u27s psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user\u27s eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An artificial eye\u27\u27 experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants

Correction and control of ocular aberrations with adaptive optics : effects on human vision

El objetivo de esta tesis doctoral es la comprensión de la relación entre la óptica de la imagen que se proyecta en la retina (en particular, la degradación impuesta por las aberraciones ópticas de alto orden) y la calidad visual. Se ha llevado a cabo el desarrollo de un nuevo sistema de Óptica Adaptativa, que incluye un sensor de frente de onda tipo Hartmann-Shack y un espejo deformable, implementando vías de psicofísica para la medida de la función visual y percepción neuronal bajo control de las aberraciones oculares mediante el espejo deformable. En esta tesis, se presenta una serie de estudios psicofísicos desarrollados para investigar el efecto de la aberraciones sobre la visión humana y la adaptación neuronal a las aberraciones oculares y también para demostrar la correlación entre la codificación interna del emborronamiento y el emborronamiento impuesto por las aberraciones de alto orden en términos de magnitud y orientació

Augmented reality fonts with enhanced out-of-focus text legibility

In augmented reality, information is often distributed between real and virtual contexts, and often appears at different distances from the viewer. This raises the issues of (1) context switching, when attention is switched between real and virtual contexts, (2) focal distance switching, when the eye accommodates to see information in sharp focus at a new distance, and (3) transient focal blur, when information is seen out of focus, during the time interval of focal distance switching. This dissertation research has quantified the impact of context switching, focal distance switching, and transient focal blur on human performance and eye fatigue in both monocular and binocular viewing conditions. Further, this research has developed a novel font that when seen out-of-focus looks sharper than standard fonts. This SharpView font promises to mitigate the effect of transient focal blur. Developing this font has required (1) mathematically modeling out-of-focus blur with Zernike polynomials, which model focal deficiencies of human vision, (2) developing a focus correction algorithm based on total variation optimization, which corrects out-of-focus blur, and (3) developing a novel algorithm for measuring font sharpness. Finally, this research has validated these fonts through simulation and optical camera-based measurement. This validation has shown that, when seen out of focus, SharpView fonts are as much as 40 to 50% sharper than standard fonts. This promises to improve font legibility in many applications of augmented reality

On-screen pre-deblurring of digital images using the wavefront aberration function of the human eye to improve computer access for the visually impaired

Traditional Optics has provided ways to compensate some common visual limitations (up to second order visual impairments) through spectacles or contact lenses. Recent developments in wavefront science make it possible to obtain an accurate model of the Point Spread Function (PSF) of the human eye. Through what is known as the Wavefront Aberration Function of the human eye, exact knowledge of the optical aberration of the human eye is possible, allowing a mathematical model of the PSF to be obtained. This model could be used to pre-compensate (inverse-filter) the images displayed on computer screens in order to counter the distortion in the user\u27s eye. This project takes advantage of the fact that the wavefront aberration function, commonly expressed as a Zernike polynomial, can be generated from the ophthalmic prescription used to fit spectacles to a person. This allows the pre-compensation, or onscreen deblurring, to be done for various visual impairments, up to second order (commonly known as myopia, hyperopia, or astigmatism). The technique proposed towards that goal and results obtained using a lens, for which the PSF is known, that is introduced into the visual path of subjects without visual impairment will be presented. In addition to substituting the effect of spectacles or contact lenses in correcting the loworder visual limitations of the viewer, the significance of this approach is that it has the potential to address higher-order abnormalities in the eye, currently not correctable by simple means

Motion blur in digital images - analys, detection and correction of motion blur in photogrammetry

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by an UAV, which have a high ground resolution and good spectral and radiometrical resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost effective and have become attractive for many applications including, change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The detection and removal of these images is currently achieved manually, which is both time consuming and prone to error, particularly for large image-sets. To increase the quality of data processing an automated process is necessary, which must be both reliable and quick. This thesis proves the negative affect that blurred images have on photogrammetric processing. It shows that small amounts of blur do have serious impacts on target detection and that it slows down processing speed due to the requirement of human intervention. Larger blur can make an image completely unusable and needs to be excluded from processing. To exclude images out of large image datasets an algorithm was developed. The newly developed method makes it possible to detect blur caused by linear camera displacement. The method is based on human detection of blur. Humans detect blurred images best by comparing it to other images in order to establish whether an image is blurred or not. The developed algorithm simulates this procedure by creating an image for comparison using image processing. Creating internally a comparable image makes the method independent of additional images. However, the calculated blur value named SIEDS (saturation image edge difference standard-deviation) on its own does not provide an absolute number to judge if an image is blurred or not. To achieve a reliable judgement of image sharpness the SIEDS value has to be compared to other SIEDS values of the same dataset. This algorithm enables the exclusion of blurred images and subsequently allows photogrammetric processing without them. However, it is also possible to use deblurring techniques to restor blurred images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears muddy and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images for deblurring may limit the application. Another method to enhance the image is the unsharp mask method, which improves images significantly and makes photogrammetric processing more successful. However, deblurring of images needs to focus on geometric correct deblurring to assure geometric correct measurements. Furthermore, a novel edge shifting approach was developed which aims to do geometrically correct deblurring. The idea of edge shifting appears to be promising but requires more advanced programming