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. Despite the pixelated stimulation, patients see smooth patterns rather than dots. We present a novel, non-pixelated algorithm for simulating prosthetic vision, compare its predictions to clinical outcomes, and describe computer vision and machine learning (ML) methods to improve face representation. Approach. Our simulation algorithm (ProViSim) integrates a spatial resolution filter based on sampling density limited by the pixel pitch and a contrast filter representing reduced contrast sensitivity of prosthetic vision. Patterns of Landolt C and human faces created using this simulator are compared to reports from actual PRIMA users. To recover the facial features lost in prosthetic vision due to limited resolution or contrast, we apply an ML facial landmarking model, as well as contrast-adjusting tone curves to the image prior to its projection onto the photovoltaic retinal implant. Main results. Prosthetic vision simulated using the above algorithm matches the letter acuity observed in clinical studies, as well as the patients' descriptions of perceived facial features. Applying the inversed contrast filter to images prior to projection onto the implant and accentuating the facial features using an ML facial landmarking model helps preserve the contrast in prosthetic vision, improves emotion recognition and reduces the response time. Significance. Spatial and contrast constraints of prosthetic vision limit the resolvable features and degrade natural images. ML based methods and contrast adjustments prior to image projection onto the implant mitigate some limitations and improve face representation.

翻译：目的：植入PRIMA光伏视网膜下假体的地理萎缩患者报告其平均视力与100微米像素尺寸匹配的形态视觉。尽管这一显著成果使他们能够读写，但患者反映在感知人脸方面存在困难。尽管受到像素化刺激，患者看到的却是平滑图案而非点状图案。我们提出了一种新颖的非像素化假体视觉模拟算法，将其预测结果与临床结果进行比较，并描述了用于改善人脸表征的计算机视觉与机器学习方法。方法：我们的模拟算法（ProViSim）整合了基于像素间距限制的采样密度空间分辨率滤波器，以及表征假体视觉对比度敏感度降低的对比度滤波器。使用该模拟器生成的Landolt C环与人脸图案与实际PRIMA使用者的报告进行了对比。为恢复因分辨率或对比度限制而在假体视觉中丢失的面部特征，我们在图像投射至光伏视网膜植入体前，应用了机器学习面部关键点检测模型以及对比度调整色调曲线。主要结果：采用上述算法模拟的假体视觉与临床研究中观察到的字母视力及患者对感知面部特征的描述相符。在图像投射至植入体前应用反向对比度滤波器，并通过机器学习面部关键点检测模型增强面部特征，有助于保持假体视觉的对比度，提升情绪识别能力并缩短响应时间。意义：假体视觉的空间与对比度约束限制了可分辨特征并降低了自然图像质量。基于机器学习的方法及图像投射前的对比度调整能够缓解部分限制，从而改善人脸表征。