用隐式神经表示实现低数据下的无透镜图像去模糊，无需预先训练。
Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime
- 引入隐式神经表示，在无训练条件下进行去模糊优化。
- 在低数据场景下性能显著优于现有方法，收敛速度更快。
- 适合对轻量化成像系统和低样本场景感兴趣的科研人员。
计算成像领域因无训练神经网络的出现迎来范式转变，为逆向计算成像问题提供新解。现有技术多依赖生成对抗网络（GAN）作为图像先验，或采用数据无关的无训练迭代重建，分别适用于高数据和无数据场景。本文聚焦无透镜图像重建——一种以计算替代传统透镜的成像方式，可实现超薄轻量成像系统。据我们所知，首次将隐式神经表示应用于无透镜图像去模糊，实现无需预训练的重建。通过嵌入先验的无训练迭代优化，提升重建性能并加速收敛，有效弥合无数据与高数据之间的差距。通过与多种无训练及低样本方法（包括参数受限的非卷积方法与领域限定的低样本方法）的全面对比，验证了本方法在性能上具有显著优势。
原文摘要 · Abstract (English)
The field of computational imaging has witnessed a promising paradigm shift with the emergence of untrained neural networks, offering novel solutions to inverse computational imaging problems. While existing techniques have demonstrated impressive results, they often operate either in the high-data regime, leveraging Generative Adversarial Networks (GANs) as image priors, or through untrained iterative reconstruction in a data-agnostic manner. This paper delves into lensless image reconstruction, a subset of computational imaging that replaces traditional lenses with computation, enabling the development of ultra-thin and lightweight imaging systems. To the best of our knowledge, we are the first to leverage implicit neural representations for lensless image deblurring, achieving reconstructions without the requirement of prior training. We perform prior-embedded untrained iterative optimization to enhance reconstruction performance and speed up convergence, effectively bridging the gap between the no-data and high-data regimes. Through a thorough comparative analysis encompassing various untrained and low-shot methods, including under-parameterized non-convolutional methods and domain-restricted low-shot methods, we showcase the superior performance of our approach by a significant margin.
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