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
