Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography

TL;DR

This paper presents a deep learning-based image restoration pipeline that improves the quality of lensless pinhole camera images by reducing blur and noise, enabling practical handheld photography and potential integration into small devices.

Contribution

It introduces a joint denoise and deblur deep learning approach tailored for pinhole camera images, enhancing image quality and usability.

Findings

Improved image sharpness and noise reduction in pinhole images.

Reduced exposure times needed for clear images.

Potential for integration into compact devices like smartphones.

Abstract

The lensless pinhole camera is perhaps the earliest and simplest form of an imaging system using only a pinhole-sized aperture in place of a lens. They can capture an infinite depth-of-field and offer greater freedom from optical distortion over their lens-based counterparts. However, the inherent limitations of a pinhole system result in lower sharpness from blur caused by optical diffraction and higher noise levels due to low light throughput of the small aperture, requiring very long exposure times to capture well-exposed images. In this paper, we explore an image restoration pipeline using deep learning and domain-knowledge of the pinhole system to enhance the pinhole image quality through a joint denoise and deblur approach. Our approach allows for more practical exposure times for hand-held photography and provides higher image quality, making it more suitable for daily photography compared to other lensless cameras while keeping size and cost low. This opens up the potential of pinhole cameras to be used in smaller devices, such as smartphones.