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Towards Lensless Image Deblurring with Prior-Embedded Implicit Neural Representations in the Low-Data Regime

arXiv 2411.18189 ↗First posted 27 Nov 2024eess.IV cs.CVcited by 0

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Abstract (arXiv)

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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References · 19

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  2. 2017Deep Image Priorcited by 1,098
  3. 2017Compressed Sensing using Generative Modelscited by 339
  4. 2021NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstructioncited by 146
  5. 2018Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networkscited by 120
  6. 2018Phase Retrieval Under a Generative Priorcited by 73
  7. 2018Compressed Sensing with Deep Image Prior and Learned Regularizationcited by 47
  8. 2019Deep Compressed Sensingcited by 46
  9. 2018Blind Image Deconvolution using Deep Generative Priorscited by 40
  10. 2017Global Guarantees for Enforcing Deep Generative Priors by Empirical Riskcited by 37
  11. 2019Image-Adaptive GAN based Reconstructioncited by 36
  12. 2019Algorithmic Guarantees for Inverse Imaging with Untrained Network Priorscited by 32
7 more
  1. 2019Learned reconstructions for practical mask-based lensless imagingcited by 31
  2. 2021Untrained networks for compressive lensless photographycited by 13
  3. 2019Global Guarantees for Blind Demodulation with Generative Priorscited by 11
  4. 2020On the interplay between physical and content priors in deep learning for computational imagingcited by 7
  5. 2018Robust Compressive Phase Retrieval via Deep Generative Priorscited by 5
  6. 2019Regularizing linear inverse problems with convolutional neural networkscited by 3
  7. 2019Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problemscited by 2

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