Abstract
Objective. Patients implanted with the PRIMA photovoltaic subretinal prosthesis in geographic atrophy report form vision with the average acuity matching the 100um pixel size. Although this remarkable outcome enables them to read and write, they report difficulty with perceiving faces. This paper provides a novel, non-pixelated algorithm for simulating prosthetic vision the way it is experienced by PRIMA patients, compares the algorithm's predictions to clinical perceptual outcomes, and offers computer vision and machine learning (ML) methods to improve face representation. Approach. Our simulation algorithm integrates a grayscale filter, spatial resolution filter, and contrast filter. This accounts for the limited sampling density of the retinal implant, as well as the reduced contrast sensitivity of prosthetic vision. Patterns of Landolt C and faces created using this simulation algorithm are compared to reports from actual PRIMA users. To recover the facial features lost in prosthetic vision, we apply an ML facial landmarking model as well as contrast adjusting tone curves to the face image prior to its projection onto the implant. Main results. Simulated prosthetic vision matches the maximum letter acuity observed in clinical studies as well as patients' subjective descriptions. Application of the inversed contrast filter helps preserve the contrast in prosthetic vision. Identification of the facial features using an ML facial landmarking model and accentuating them further improve face representation. Significance. Spatial and contrast constraints of prosthetic vision limit resolvable features and degrade natural images. ML based methods and contrast adjustments mitigate some limitations and improve face representation. Even though higher spatial resolution can be expected with implants having smaller pixels, contrast enhancement still remains essential for face recognition.
Abstract (translated)
目标。植入PRIMA光伏亚视网膜假体的患者报告称，他们的视力与100微米像素大小匹配的平均清晰度相似。尽管这种显著的结果使他们能够阅读和书写，但他们表示在感知面部方面存在困难。本文提供了一种用于模拟假肢视觉的新算法（该算法类似于PRIMA患者体验到的视觉），将此算法预测结果与临床感知效果进行比较，并提出计算机视觉和机器学习(ML)方法以改善面部表现。 方法。我们的模拟算法结合了灰度滤波器、空间分辨率滤波器和对比度滤波器，这可以解释视网膜植入物有限的采样密度以及假肢视力减少的对比敏感度。使用此仿真算法创建的Landolt C图案及面孔与实际PRIMA用户报告进行了比较。为了恢复在假肢视觉中丢失的脸部特征，我们在将图像投影到假体之前应用了机器学习面部标记模型和调整对比度的色调曲线。 主要结果。模拟的假体视觉匹配临床研究观察到的最大字母清晰度以及患者的主观描述。使用反向对比滤波器有助于保留假体视觉中的对比度。使用ML面部地标模型识别面部特征并进一步强调它们可以改善面部表现。 意义。由于空间和对比限制，假体视觉在可分辨的特性方面受到限制，并且会降解自然图像。基于机器学习的方法和对比调整能够缓解一些局限性并改善面部表示。尽管具有更小像素的植入物可能具有更高的空间分辨率，但对比度增强仍然是面部识别中的关键因素。
URL
https://arxiv.org/abs/2503.11677
