Topology optimization using super-resolution image reconstruction methods

This paper proposes a new topology optimization method to obtain super-resolution images without increasing mesh refinement by using various methods. For traditional process, low-resolution (LR) are fed into the Solid Isotropic Material with Penalization (SIMP) and Optimality Criteria (OC) Here, trained added inner loops reconstruct used high-resolution (HR) from LR at end of each iteration. After finishing reconstruction main recovers original size HR for next Several examples presented demonstrate effectiveness proposed method. The final topologies provide noticeably improvement over those typical SIMP create much sharper higher contrast images. Moreover, strategy image methods can give valuable innovation conventional process.