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.

Automated summary

Thermo-mechanical treatments are important for improving the quality of metals by enabling the evolution of metal microstructures. Computer-based simulations of these treatments have become popular in the metallurgy industry due to their efficiency and accuracy. In this study, a generative adversarial network architecture is proposed for denoising steel microstructure images, which shows better performance compared to state-of-the-art techniques.