Learning in-place residual homogeneity for single image detail enhancement

Abstract

An image detail enhancement algorithm is proposed based on in-place residual homogeneity (IP). Residual homogeneity is a physical law, which mainly explains the texture similarity between the same image residual at slightly different resolutions. As we all know, a single image can be divided into a base layer and a detail layer, and the effective estimation of the detail layer is the key in a detail enhancement algorithm. In the experiment, we find that the residual layer of an image obtained by bilinear interpolation is closely related to its detail layer, hence it can be used as the initial estimation of the detail layer, then residual homogeneity is applied to update the residual layer until the accurate detail layer is acquired. In the process of updating residuals, a searching method called fast in-place searching (FIPS) is used. FIPS only takes advantage of the residual homogeneity within the in-place region, which accelerates the project about 93%. Different from the local-based and global-based methods, our IP gets the detail layer directly and amplifies it. It has many good properties, such as being fast, edge-aware, robust, and parameter-free. Good performance has been demonstrated on several widely used datasets by both subjective and objective evaluations.