Generative Adversarial Networks for Noise Removal in Plain Carbon Steel Microstructure Images
Aditi Panda, Ruchira Naskar, Snehanshu Pal · IEEE Sensors Letters · 2022
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 simulationsof 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 withdigital microstructure images, accessible in proper digitized forms. Taking that into consideration, we propose agenerative adversarial networkarchitecture for denoising steel microstructure images. Experimental results demonstrate the efficacy of the proposed model in comparison to the contemporary state-of-the-art techniques.