The Advanced Technology Microwave Sounder (ATMS) is an important satellite instrument that provides vital data on atmosphere temperature and moisture for weather forecasting, climate research, and help us plan for extreme weather. However, its coarse resolution and angular dependence have long been a challenge for improving image visualization. This study proposes a method to enhance the imagery visualization for ATMS, combining limb correction with artificial intelligence (AI) resolution enhancement. Measurement data from the ATMS onboard NOAA-20 was utilized to train the limbcorrection method, which was then validated using newly acquired NOAA-21 ATMS data. The AI resolution enhancement was performed using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), which increased the pixel resolution by a factor of four. The high-resolution Advanced Microwave Scanning Radiometer 2 (AMSR2) data served as a reference to initially and quantitatively evaluate the resolution enhancement method. The combined method of limb correction and AI resolution enhancement produced an angular-dependence-free and high-resolution ATMS image, resulting in a significant improvement in image visualization, including surface and atmosphere information, and allows for clear identification of severe weather events. For the swift identification and analysis of tropical cyclones in the upcoming season, as of this writing, this proposed method has been routinely employed to produce high-quality global ATMS images, and these images are showcased and tested in the NOAA internal high-resolution imagery visualization system --JSTAR Mapper. Moreover, concentrated efforts are being made to further enhance these images in preparation for an official release.