A Modular and Robust Physics-Based Approach for Lensless Image Reconstruction
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916410440024064 |
|---|---|
| author | Perron, Yohann Bezzam, Eric Vetterli, Martin |
| author_facet | Perron, Yohann Bezzam, Eric Vetterli, Martin |
| contents | In this paper, we present a modular approach for reconstructing lensless measurements. It consists of three components: a newly-proposed pre-processor, a physics-based camera inverter to undo the multiplexing of lensless imaging, and a post-processor. The pre- and post-processors address noise and artifacts unique to lensless imaging before and after camera inversion respectively. By training the three components end-to-end, we obtain a 1.9 dB increase in PSNR and a 14% relative improvement in a perceptual image metric (LPIPS) with respect to previously proposed physics-based methods. We also demonstrate how the proposed pre-processor provides more robustness to input noise, and how an auxiliary loss can improve interpretability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Modular and Robust Physics-Based Approach for Lensless Image Reconstruction Perron, Yohann Bezzam, Eric Vetterli, Martin Image and Video Processing In this paper, we present a modular approach for reconstructing lensless measurements. It consists of three components: a newly-proposed pre-processor, a physics-based camera inverter to undo the multiplexing of lensless imaging, and a post-processor. The pre- and post-processors address noise and artifacts unique to lensless imaging before and after camera inversion respectively. By training the three components end-to-end, we obtain a 1.9 dB increase in PSNR and a 14% relative improvement in a perceptual image metric (LPIPS) with respect to previously proposed physics-based methods. We also demonstrate how the proposed pre-processor provides more robustness to input noise, and how an auxiliary loss can improve interpretability. |
| title | A Modular and Robust Physics-Based Approach for Lensless Image Reconstruction |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2403.00537 |