Enhanced Edge-Perceptual Guided Image Filtering
Jinyu Li
Due to the powerful edge-preserving ability and low computational complexity,
Guided image filter (GIF) and its improved versions has been widely applied in
computer vision and image processing. However, all of them are suffered halo
artifacts to some degree, as the regularization parameter increase. In the case
of inconsistent structure of guidance image and input image, edge-preserving
ability degradation will also happen. In this paper, a novel guided image
filter is proposed by integrating an explicit first-order edge-protect
constraint and an explicit residual constraint which will improve the
edge-preserving ability in both cases. To illustrate the efficiency of the
proposed filter, the performances are shown in some typical applications, which
are single image detail enhancement, multi-scale exposure fusion, hyper
spectral images classification. Both theoretical analysis and experimental
results prove that the powerful edge-preserving ability of the proposed filter.