Influence of blur on feature matching and a geometric approach for photogrammetric deblurring

Unmanned aerial vehicles (UAV) have become an interesting and active research topic for photogrammetry. Current research is based on images acquired by a UAV, which have a high ground resolution and good spectral and radiometric resolution, due to the low flight altitudes combined with a high resolution camera. UAV image flights are also cost efficient and have become attractive for many applications including change detection in small scale areas. One of the main problems preventing full automation of data processing of UAV imagery is the degradation effect of blur caused by camera movement during image acquisition. This can be caused by the normal flight movement of the UAV as well as strong winds, turbulence or sudden operator inputs. This blur disturbs the visual analysis and interpretation of the data, causes errors and can degrade the accuracy in automatic photogrammetric processing algorithms. The aim of this research is to develop a blur correction method to deblur UAV images. Deblurring of images is a widely researched topic and often based on the Wiener or Richardson-Lucy deconvolution, which require precise knowledge of both the blur path and extent. Even with knowledge about the blur kernel, the correction causes errors such as ringing, and the deblurred image appears "muddy" and not completely sharp. In the study reported in this paper, overlapping images are used to support the deblurring process, which is advantageous. An algorithm based on the Fourier transformation is presented. This works well in flat areas, but the need for geometrically correct sharp images may limit the application. Deblurring images needs to focus on geometric correct deblurring to assure geometric correct measurements.