Deep Camera Obscura: An Image Restoration Pipeline for Lensless Pinhole Photography
This work addresses the limitations of pinhole cameras for photography applications, making them more viable for use in smaller devices like smartphones, though it is incremental as it builds on existing deep learning and domain-knowledge methods.
The paper tackles the problem of low sharpness and high noise in lensless pinhole cameras by developing a deep learning-based image restoration pipeline for joint denoising and deblurring, enabling more practical exposure times and higher image quality for daily photography.
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.