无需训练的视网膜植入编码方法，提升视觉重建质量。
Training-Free Stimulus Encoding for Retinal Implants via Sparse Projected Gradient Descent
- 将刺激编码建模为稀疏最小二乘问题，利用感知模型约束。
- 在15×15至100×100电极下，SSIM提升0.265，PSNR增12.4dB。
- 适用于高精度视网膜假体，尤其适合无训练场景的实时应用。
视网膜植入旨在恢复光感受器退化导致的功能性视力，但受限于低分辨率电极阵列和患者特异性感知畸变。现有编码器多依赖任务无关的降采样与亮度到振幅的线性映射，无法在真实感知模型下达到最优。尽管全局逆问题可由神经网络求解，但需训练且泛化能力有限。本文将刺激编码建模为线性感知前向模型下的约束稀疏最小二乘问题，发现感知矩阵具有高度稀疏性，取决于患者与植入配置。基于此，提出一种高效的投影残差范数最陡下降求解器，利用稀疏性并支持通过投影施加刺激边界。在四个模拟患者、15×15至100×100电极的仿真实验中，相比Lanczos降采样，Fashion-MNIST重建的SSIM提升0.265，PSNR增加12.4 dB，MAE降低81.4%。
原文摘要 · Abstract (English)
Retinal implants aim to restore functional vision despite photoreceptor degeneration, yet are fundamentally constrained by low resolution electrode arrays and patient-specific perceptual distortions. Most deployed encoders rely on task-agnostic downsampling and linear brightness-to-amplitude mappings, which are suboptimal under realistic perceptual models. While global inverse problems have been formulated as neural networks, such approaches can be fast at inference, and can achieve high reconstruction fidelity, but require training and have limited generalizability to arbitrary inputs. We cast stimulus encoding as a constrained sparse least-squares problem under a linearized perceptual forward model. Our key observation is that the resulting perception matrix can be highly sparse, depending on patient and implant configuration. Building on this, we apply an efficient projected residual norm steepest descent solver that exploits sparsity and supports stimulus bounds via projection. In silico experiments across four simulated patients and implant resolutions from $15\times15$ to $100\times100$ electrodes demonstrate improved reconstruction fidelity, with up to $+0.265$ SSIM increase, $+12.4\,\mathrm{dB}$ PSNR, and $81.4\%$ MAE reduction on Fashion-MNIST compared to Lanczos downsampling.
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