Generative Adversarial Networks for Noise Removal in Plain Carbon Steel Microstructure Images

Thermo-mechanical treatments are employed to bring about variety in the quality of metals. These treatments only work when carried out in accordance with appropriate schedules, so as to enable the evolution of metal microstructures in a suitable way. Recently,
computer-based simulations
of these treatments have become hugely desired in the metallurgy industry, due to their time and resource efficiency, and also because they are free from manual experimentation errors. However, such simulations are realizable only with
digital microstructure images
, accessible in proper digitized forms. Taking that into consideration, we propose a
generative adversarial network
architecture for denoising steel microstructure images. Experimental results demonstrate the efficacy of the proposed model in comparison to the contemporary state-of-the-art techniques.