Hierarchy of nonlocal means for preferred automatic sharpness enhancement and tone mapping

Abstract

Existing hierarchical techniques that decompose an image into a smooth image and high frequency components based on Gaussian filter and bilateral filter suffer from halo effects, whereas techniques based on weighted least squares extract low contrast features as details. Other techniques require multiple images and are not tolerant to noise. We use a single image to enhance sharpness based on a hierarchical framework using a modified Laplacian pyramid. In order to ensure robustness, we remove noise by using an extra level in the hierarchical framework. We use an edge-preserving nonlocal means filter and modify it to remove potential halo effects and gradient reversals. However, these effects are only reduced but not removed completely after similar modifications are made to the bilateral filter. We compare our results with existing techniques and show better decomposition and enhancement. Based on validation by human observers, we introduce a new measure to quantify sharpness quality, which allows us to automatically set parameters in order to achieve preferred sharpness enhancement. This causes blurry images to be sharpened more and sufficiently sharp images not to be sharpened. Finally, we demonstrate applications in the context of robust high dynamic range tone mapping that is better than state-of-the-art approaches and enhancement of archaeological artifacts.