<p>This study introduces an optical neural network (ONN)-based autoencoder for efficient image processing, utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks. To address the challenges in efficient decoding, we propose a method that optimizes output processing through scalar multiplications, enhancing performance in generating higher-dimensional outputs. By employing on-system iterative tuning, we mitigate hardware imperfections and noise, progressively improving image reconstruction accuracy to near-digital quality. Furthermore, our approach supports noise reduction and optical image generation, enabling models such as denoising autoencoders, variational autoencoders, and generative adversarial networks. Our results demonstrate that ONN-based systems have the potential to surpass the energy efficiency of traditional electronic systems, enabling real-time, low-power image processing in applications such as medical imaging, autonomous vehicles, and edge computing.</p>