Image Processing Application for Cognition (IPAC) - Traditional and Emerging Topics in Image Processing in Astronomy

Abstract

A new application framework for advanced image processing for astronomy is presented. It implements standard two-dimensional operators, and recent developments in the field of non-astronomical image processing (IP), as well as original algorithms based on nonlinear partial differential equations (PDE). These algorithms are especially well suited for multi-scale astronomical images since they increase signal to noise ratio without smearing localized and diffuse objects. The visualization component is based on the extensive tools that we developed for Spitzer Space Telescope’s observation planning tool Spot and archive retrieval tool Leopard. It contains many common features, combines images in new and unique ways and interfaces with many astronomy data archives. Both interactive and batch mode processing are incorporated. In the interactive mode, the user can set up simple processing pipelines, and monitor and visualize the resulting images from each step of the processing stream. The system is platform-independent and has an open architecture that allows extensibility by addition of plug-ins. This presentation addresses astronomical applications of traditional topics of IP (image enhancement, image segmentation) as well as emerging new topics like automated image quality assessment (QA) and feature extraction, which have potential for shaping future developments in the field. Our application framework embodies a novel synergistic approach based on integration of image processing, image visualization and image QA (iQA).