DRIP: An R Package for Jump Regression and Image Analysis

Jump regression performs nonparametric regression analysis where the regression function can be discontinuous. Because of its ability to conduct jump detection and jump-preserving estimation, jump regression provides a natural statistical framework for image analysis. It enables statistical inference involved in many image processing problems including edge detection, image denoising and image deblurring. As the jump regression literature grows over the past two decades, the methodological development has far outpaced the availability of jump regression software. Although there are many image processing software programs, most of them implement either generic image operations or statistical analysis of images of specific types. In this article, we introduce DRIP, an R package with a number of state-of-the-art jump regression methods for jump detection and surface reconstruction. Its open-source nature and user-friendly interface make it convenient to develop and evaluate jump regression methods, encouraging wider adoption and future research. Although the current version of DRIP supports analysis of monochrome images only, as the literature on multivariate jump regression is still lacking, our methods can be applied separately to each color channel when handling color images.