Perceptually motivated automatic sharpness enhancement using hierarchy of non-local means

We address the problem of sharpness enhancement of images. 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 detail. 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 Non-Local Means filter and modify it to remove potential halo effects. However, these effects are only reduced but not removed completely after similar modifications are made to bilateral filter. We compare our results with existing techniques, including commercial packages and show better decomposition and enhancement using our method. 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 to not be sharpened.