Enhanced Edge-Perceptual Guided Image Filtering
This work addresses image quality issues in computer vision applications, but it is incremental as it builds upon existing guided filter methods.
The paper tackles halo artifacts and edge-preserving degradation in guided image filters by proposing a novel filter with explicit constraints, resulting in improved edge-preserving ability demonstrated in applications like detail enhancement and exposure fusion.
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.