Helsinki deblur challenge 2021: Description of photographic data

Image deconvolution is a classical inverse problem that serves well as a computational test bench for reconstruction algorithms. Namely, the direct operator can be modelled in a straightforward way either by convolution or by multiplication in the frequency domain. Further, the ill-posedness of the inverse problem can be adjusted by the form of the point spread function (PSF). An open photographic dataset is described, suitable for testing practical deconvolution methods. The image material was designed and collected for the Helsinki Deblur Challenge 2021. The dataset contains pairs of images taken by two identical cameras of the same target but with different conditions. One camera is always in focus and generates sharp and low-noise images, while the other camera produces blurred and noisy photos as it is gradually more and more out of focus and has a higher ISO setting. The data is available here: https://doi.org/10.5281/zenodo.4916176