Automatic Estimation of Modulation Transfer Functions
This provides a cost-effective solution for photographers and optical engineers to characterize lens performance, though it is incremental as it builds on existing data-driven approaches.
The paper tackles the problem of estimating the modulation transfer function (MTF) of camera lenses without expensive equipment by developing a method that uses a convolutional neural network to estimate MTF directly from photographs, achieving generalization to unseen lenses and improved performance with multiple images.
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.