Shape Deblurring with Unsharp Masking Applied to Mesh Normals

Unsharp masking is a well-known image sharpening technique. Given an image and its smoothed version, amplifying high frequencies of the image via unsharp masking is achieved by linear extrapolation of the input images. In this paper, we adapt the unsharp masking technique for 3D shape deblurring purposes. Consider a blurred shape represented by a triangle mesh. Usually such a shape results from a 3D data corrupted by noise and then oversmoothed. First we apply unsharp masking to the mesh normals. To smooth the filed of mesh normals we use several local averaging iterations applied to the mesh normals (iterative mean filtering). Then we apply linear extrapolation of the original and smoothed fields of normals. Finally we reconstruct the deblurred mesh by integrating the field of extrapolated normals. We also give a quantitative evaluation of the proposed unsharp masking technique. To perform the evaluation, we use $L^2$ error metrics on mesh vertices and normals. Experimental results show that the unsharp masking technique is effective for shape deblurring