A generative approach for lensless imaging in low-light conditions
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910780578856960 |
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| author | Liu, Ziyang Zeng, Tianjiao Zhan, Xu Zhang, Xiaoling Lam, Edmund Y. |
| author_facet | Liu, Ziyang Zeng, Tianjiao Zhan, Xu Zhang, Xiaoling Lam, Edmund Y. |
| contents | Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of a focusing lens and limited lighting in such environments often result in low-light conditions, where the measurements suffer from complex noise interference due to insufficient capture of photons. This study presents a robust reconstruction method for high-quality imaging in low-light scenarios, employing two complementary perspectives: model-driven and data-driven. First, we apply a physic-model-driven perspective to reconstruct in the range space of the pseudo-inverse of the measurement model as a first guidance to extract information in the noisy measurements. Then, we integrate a generative-model based perspective to suppress residual noises as the second guidance to suppress noises in the initial noisy results. Specifically, a learnable Wiener filter-based module generates an initial noisy reconstruction. Then, for fast and, more importantly, stable generation of the clear image from the noisy version, we implement a modified conditional generative diffusion module. This module converts the raw image into the latent wavelet domain for efficiency and uses modified bidirectional training processes for stabilization. Simulations and real-world experiments demonstrate substantial improvements in overall visual quality, advancing lensless imaging in challenging low-light environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A generative approach for lensless imaging in low-light conditions Liu, Ziyang Zeng, Tianjiao Zhan, Xu Zhang, Xiaoling Lam, Edmund Y. Image and Video Processing Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of a focusing lens and limited lighting in such environments often result in low-light conditions, where the measurements suffer from complex noise interference due to insufficient capture of photons. This study presents a robust reconstruction method for high-quality imaging in low-light scenarios, employing two complementary perspectives: model-driven and data-driven. First, we apply a physic-model-driven perspective to reconstruct in the range space of the pseudo-inverse of the measurement model as a first guidance to extract information in the noisy measurements. Then, we integrate a generative-model based perspective to suppress residual noises as the second guidance to suppress noises in the initial noisy results. Specifically, a learnable Wiener filter-based module generates an initial noisy reconstruction. Then, for fast and, more importantly, stable generation of the clear image from the noisy version, we implement a modified conditional generative diffusion module. This module converts the raw image into the latent wavelet domain for efficiency and uses modified bidirectional training processes for stabilization. Simulations and real-world experiments demonstrate substantial improvements in overall visual quality, advancing lensless imaging in challenging low-light environments. |
| title | A generative approach for lensless imaging in low-light conditions |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2501.03511 |