Image Pre-compensation Research Articles

Development of Image Pre-Processing Methods for Software Compensation of Anomal Refraction of the Observer’s Eyes

In recent decades, the practice of demonstrating various static and video images to users using digital, processor-controlled, most often self-luminous devices (computer monitors, smartphone and tablet screens, etc.) has spurred the development of various methods to improve the perception of such images by means of computerized image preprocessing. This also applies to methods of preprocessing images shown to users with various refractive anomalies of the eye(s) (e.g., myopia or astigmatism) in situations where they are not armed with glasses or other corrective devices. Over the past 20+ years, researchers have published dozens of papers on this task, referred to as the precompensation task. In our opinion, the time has come to reflect on the development of scientific thought in this direction and to highlight the most important milestones in realizing the problems on the way to achieving “ideal” precompensation and in approaches to their successful solution. This is the focus of the first part of this review. In the second part, we focus on the current state of research in the stated area, highlight the problems not solved so far, and try to catch the trends of further development of image precompensation methods, paying maximum attention to neural network approaches.

Content Aware Image Pre-Compensation.

The goal of image pre-compensation is to process an image such that after being convolved with a known kernel, will appear close to the sharp reference image. In a practical setting, the pre-compensated image has significantly higher dynamic range than the latent image. As a result, some form of tone mapping is needed. In this paper, we show how global tone mapping functions affect contrast and ringing in image pre-compensation. We further enhance contrast and reduce ringing by considering the visual saliency. Specifically, we prioritize contrast preservation in salient regions while tolerating more blurriness elsewhere. For quantitative analysis, we design new metrics to measure the contrast of an image with ringing. Specifically, we set out to find its "equivalent ringing-free" image that matches its intensity histogram and uses its contrast as the measure. We illustrate our approach on projector defocus compensation and visual acuity enhancement. Compared with the state-of-the-art, our approach significantly improves the contrast. We also perform user studies to demonstrate that our method can effectively improve the viewing experience for users with impaired vision.

Personalised and dynamic image precompensation for computer users with ocular aberrations

Most of the computer users with ocular aberrations such as myopia, hyperopia and other high-order aberrations are subject to visual blurring, which may impede the efficient interactions with the computers. Conventional methods used to counter visual blurring include spectacles and contact lenses. In this paper, we introduce an image preprocessing method that is designed to counteract the visual blurring caused by the aberration of the eye. In this method, the presented images are preprocessed by performing personalised compensation according to the ocular aberration of the computer user. In order to overcome the mismatch between the aberration used to generate the precompensation and the actual aberrations at the time of viewing, dynamic ocular aberrations are derived from the resizing of the initial aberration data measured by the wavefront analyzer. The dynamic ocular aberrations are used to update the image precompensation in real time. Results of human subject experiment show that the image recognition accuracy was significantly increased after the dynamic precompensation was applied. Subjective impressions from the participants confirmed the effectiveness of the method.

Image Pre-compensation Algorithm for Monochromatic Aberrations in the Eyes

Monochromatic aberrations can limit visual performance of the human eyes. Visual limitations caused by higher-order aberrations can not be corrected by traditional optical correction methods such as spectacles and contact lens. In this paper, we address the problem of correcting visual limitations based on an image deconvolution technique. First, we introduce Zernike polynomials to describe the wavefront aberration. Then, the point spread function (PSF) of the human eyes can be calculated by using the wavefront aberration function. Finally, we propose an image pre-compensation algorithm based on the knowledge of the PSF to improve a blurred image formed by the aberrated eyes. Experimental results are presented to demonstrate the potential visual improvement that can be achived by our pre-compensation algorithm.

Image pre-compensation to facilitate computer access for users with refractive errors

Computer technologies, frequently employed for everyday tasks, often use Graphical User Interfaces (GUIs), presented through monitors or LCD displays. This type of visual interface is not well suited for users with refractive visual limitations, particularly when they are severe and not correctable by common means. In order to facilitate computer access for users with refractive deficiencies, an algorithm has been developed, using a priori knowledge of the visual aberration, to generate on-screen images that counter the effect of the aberration. When the user observes the screen displaying a pre-compensated image, the image perceived in the retina will be similar to the original image. The algorithm was tested by artificially introducing a spherical aberration in the field of view of 14 subjects, totaling 28 individual eyes. This use of pre-compensation improves the visual performance of the subjects with respect to that achieved with no compensation.

Image pre-compensation to facilitate computer access for users with refractive errors

The use of computer technology for everyday tasks has become increasingly important in today's world. Frequently, computer technology makes use of Graphical User Interfaces (GUIs), presented through monitors or LCD displays. This type of visual interface is not well suited for users with visual limitations due to refractive errors, particularly when they are severe and not correctable by common means. In order to facilitate computer access for users with refractive deficiencies, an algorithm was developed, using a priori knowledge of the visual aberration, to generate an inverse transformation of the images that are then displayed on-screen, countering the effect of the aberration. The result is that when the user observes the screen displaying the transformed images, the image perceived in the retina will be similar to the original image. The algorithm was tested by artificially introducing a spherical aberration in the field of view of 14 subjects, totaling 28 individual eyes. Results show that when viewing the screen, this method of compensation improves the visual performance of the subjects tested in comparison to viewing uncompensated images.