Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements. To tackle this challenge, we introduce a high-dimensional neural representation of blur—the lens blur field—and a practical method for acquisition.
The lens blur field is a multilayer perceptron (MLP) designed to (1) accurately capture variations of the lens 2D point spread function over image plane location, focus setting and, optionally, depth and (2) represent these variations parametrically as a single, sensor-specific function. The representation models the combined effects of defocus, diffraction, aberration, and accounts for sensor features such as pixel color filters and pixel-specific micro-lenses. To learn the real-world blur field of a given device, we formulate a generalized non-blind deconvolution problem that directly optimizes the MLP weights using a small set of focal stacks as the only input.
We provide a first-of-its-kind dataset of 5D blur fields—for smartphone cameras, camera bodies equipped with a variety of lenses, etc. Finally, we show that acquired 5D blur fields are expressive and accurate enough to reveal, for the first time, differences in optical behavior of smartphone devices of the same make and model.
The lens blur field enables accurate, fast, and lightweight acquisition of high-dimensional point spread function (PSF) for diverse real-life camera systems. It can be constructed through the following steps: (1) capturing focal stacks of a monitor, (2) isolating for defocus effects through lens distortion, magnification, and radiometric compensations, and (3) training the MLP.
@article{lin2023learning,
author = {Lin, Esther Y.H. and Wang, Zhecheng and Lin, Rebecca and Maui, Daniel and Kainz, Florian and Chen, Jiawen and Zhang, Xuaner and Lindell, David B. and Kutulakos, Kiriakos N.},
title = {Learning Lens Blur Fields},
journal = {arXiv},
year = {2023},
}