From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
In this paper, we propose a unified optimization framework for L2, L1, and/or L0 constrained image reconstruction. First, we generalize cost functions for image reconstruction, which consist of a fidelity term with L2 norm and constraint terms with L2, L1, and/or L0 norms. This generalized cost function covers many types of existing cost functions for image reconstruction. Then, we show that this generalized cost function can be optimized by the alternating direction method of multipliers (ADMM). The ADMM is a well-known iterative optimization approach for convex problems. Experimental results demonstrate that the proposed unified optimization framework is applicable to a wide range of applications.
Masayuki Tanaka and Masatoshi Okutomi, "Unified optimization framework for L2, L1, and/or L0 constrained image reconstruction," Proc. SPIE 10222, Computational Imaging II, 102220J (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 10, 2017; Published: 1 May 2017); https://doi.org/10.1117/12.2257957.
Conference Presentations are recordings of oral presentations given at SPIE conferences and published as part of the proceedings. They include the speaker's narration with video of the slides and animations. Most include full-text papers. Interactive, searchable transcripts and closed captioning are now available for 2018 presentations, with transcripts for prior recordings added daily.
Search our growing collection of more than 16,000 conference presentations, including many plenaries and keynotes.