Deep camera obscura: an image restoration pipeline for pinhole photography

Joshua D. Rego, Huaijin Chen, Shuai Li, Jinwei Gu, Suren Jayasuriya

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Modern machine learning has enhanced the image quality for consumer and mobile photography through low-light denoising, high dynamic range (HDR) imaging, and improved demosaicing among other applications. While most of these advances have been made for normal lens-based cameras, there has been an emerging body of research for improved photography for lensless cameras using thin optics such as amplitude or phase masks, diffraction gratings, or diffusion layers. These lensless cameras are suited for size and cost-constrained applications such as tiny robotics and microscopy that prohibit the use of a large lens. However, the earliest and simplest camera design, the camera obscura or pinhole camera, has been relatively overlooked for machine learning pipelines with minimal research on enhancing pinhole camera images for everyday photography applications. In this paper, we develop an image restoration pipeline of the pinhole system to enhance the pinhole image quality through joint denoising and deblurring. Our pipeline integrates optics-based filtering and reblur losses for reconstructing high resolution still images (2600 × 1952) as well as temporal consistency for video reconstruction to enable practical exposure times (30 FPS) for high resolution video (1920 × 1080). We demonstrate high 2D image quality on real pinhole images that is on-par or slightly improved compared to other lensless cameras. This work opens up the potential of pinhole cameras to be used for photography in size-limited devices such as smartphones in the future.

Original languageEnglish (US)
Pages (from-to)27214-27235
Number of pages22
JournalOptics Express
Issue number15
StatePublished - Jul 18 2022

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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