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Image mosaicing is a technique widely used for extending the field of view of industrial, medical, outdoor or indoor scenes. However, image registration can be very challenging, e.g. due to large texture variability, illumination changes, image blur and camera perspective changes. In this paper, a total variational optical flow approach is investigated to estimate dense point correspondences between image pairs. An edge preserving Riesz wavelet scale-space combined with a novel TV-regularizer is proposed for preserving motion discontinuities along the edges of weak textures and for handling strong in-plane rotations present in image sequences. An anisotropic weighted median filtering is implemented for minimizing outliers. Quantitative evaluation of the method on the Middlebury image database and simulated sequences with known ground truth demonstrates high accuracy of the proposed method in comparison with other state-of-the-art methods, including a robust graph-cut method and a patch matching approach. Qualitative results on video-sequences of difficult real scenes demonstrate the robustness of the proposed method. HighlightsMosaicing of images with strong texture and illumination variability.Fast, robust and accurate optical flow computation.TV-L1 method on a second order Riesz wavelet basis for preserving texture discontinuities.Novel anisotropic TV-regularizer.Accurate results for both the Middlebury data set and various complicated scenes.

Image registration is a crucial task in many applications and applied in a variety of different areas. In addition to the primary task of image alignment, the deformation field is valuable when studying structural/volumetric changes in the brain. In most applications a regularizing term is added to achieve a smoothly varying deformation field. This can sometimes cause conflicts in situations of local complex deformations. In this paper we present a new regularizer, which aims at handling local complex deformations while maintaining an overall smooth deformation field. It is based on an adaptive anisotropic regularizer and its usefulness is demonstrated by two examples, one synthetic and one with real MRI data from a pre- and post-op situation with normal pressure hydrocephalus.

Image registration is a crucial task in many applications and applied in a variety of different areas. In addition to the primary task of image alignment, the deformation field is valuable when studying structural/volumetric changes in the brain. In most applications a regularizing term is added to achieve a smoothly varying deformation field. This can sometimes cause conflicts in situations of local complex deformations. In this paper we present a new regularizer, which aims at handling local complex deformations while maintaining an overall smooth deformation field. It is based on an adaptive anisotropic regularizer and its usefulness is demonstrated by two examples, one synthetic and one with real MRI data from a pre- and post-op situation with normal pressure hydrocephalus.