Automatic Estimation of Modulation Transfer Functions

TL;DR

This paper introduces a data-driven method using neural networks to estimate the modulation transfer function (MTF) of camera lenses directly from photographs, eliminating the need for costly measurement equipment and enabling practical, on-the-fly lens performance assessment.

Contribution

It presents a novel supervised learning approach that estimates MTF from natural images, generalizes to unseen lenses, and improves with multiple photographs, bypassing traditional costly measurement methods.

Findings

Accurately estimates MTF from single images.

Generalizes well to unseen lenses.

Performance improves with multiple images.

Abstract

The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen. Instead, MTFs based on simulations or, at best, MTFs measured on other specimens of the same lens are used. Fortunately, images recorded through an optical system contain ample information about its MTF, only that it is confounded with the statistics of the images. This work presents a method to estimate the MTF of camera lens systems directly from photographs, without the need for expensive equipment. We use a custom grid display to accurately measure the point response of lenses to acquire ground truth training data. We then use the same lenses to record natural images and employ a data-driven supervised learning approach using a convolutional neural network to estimate the MTF on small image patches, aggregating the information into MTF charts over the entire field of view. It generalises to unseen lenses and can be applied for single photographs, with the performance improving if multiple photographs are available.