An Adaptive Bilateral Filter for Inpainting
Dao Nam Anh · 2014
An adaptive model of bilateral filter is presented for digital in painting. The model works by transforming in painting into an equivalent energy condition minimization and generation of patches for missing areas by interpolating within working frame. It combines knowledge of local structure by bilateral filter and intensive value. Bilateral filter is adapted to missing regions to check similarity of regions to fill-in. Standard deviation in range kernel of the filter is regulated by total variation. This helps to create patches that keep edges. Total variation is also efficient for detection of missing pixels which possibly stay on edges. Benefit of the model was demonstrated in experiment of in painting for gray and color images.