Kernel smoothing for jagged edge reduction
Mohammad Aghagolzadeh, Andrew Segall · 2013
In this paper, we consider the problem of removing jaggy artifacts from images. We consider the kernel regression framework and propose a reduced-rank quadratic adaptive method that adapts to the local gradient direction. The proposed technique is effective in shrinking isophote fluctuations, and the result is smooth edges. We observe that it is critical to differentiate jaggy artifacts from texture, junctions and corners, so that meaningful image structure is preserved. Here, we demonstrate that the spectrum of the local covariance matrix of gradients, also known as the structure tensor, is well suited for differentiation of jaggy artifacts from image structure, and we incorporate this into the kernel regression framework. Results show the efficacy of the approach. Namely, that the method is effective in reducing jaggy artifacts without blurring meaningful image structure.