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<p>Recent advances in lensless imaging reconstruction have primarily relied on supervised neural models trained using target images captured by lensed cameras via a beam splitter. However, we argue that using reference images from a different optical system introduces bias into the reconstruction process. To mitigate this issue, we propose a self-supervised approach that leverages data-fidelity guidance, similar to deep image prior, to train neural models for single-iteration lensless reconstruction. Through simulations and prototype camera experiments, we demonstrate that combining simple convex optimization methods with a denoising UNet improves perceptual quality (LPIPS), accelerates inference compared to traditional optimization techniques, and reduces potential unwanted biases in the reconstruction network.</p>

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Self-supervised neural reconstructions for lensless imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月30日

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  • Jose Reinaldo Cunha Santos Aroso Vieira Silva Neto,
  • Hodaka Kawachi,
  • Yasushi Yagi,
  • Tomoya Nakamura

摘要

Recent advances in lensless imaging reconstruction have primarily relied on supervised neural models trained using target images captured by lensed cameras via a beam splitter. However, we argue that using reference images from a different optical system introduces bias into the reconstruction process. To mitigate this issue, we propose a self-supervised approach that leverages data-fidelity guidance, similar to deep image prior, to train neural models for single-iteration lensless reconstruction. Through simulations and prototype camera experiments, we demonstrate that combining simple convex optimization methods with a denoising UNet improves perceptual quality (LPIPS), accelerates inference compared to traditional optimization techniques, and reduces potential unwanted biases in the reconstruction network.