DeepEMhancer is a deep learning approach for cryo-EM volume post-processing. Cryo-EM maps are valuable for protein structure modeling but often require post-processing due to high-frequency contrast loss. Traditional methods like global B-factor correction have limitations, such as ignoring local map quality variations. DeepEMhancer, trained on experimental maps and sharpened maps using atomic models, performs automatic post-processing with masking and sharpening-like operations in one step. It was evaluated on 20 experimental maps, showing improved noise reduction and detailed map versions. DeepEMhancer also enhanced the SARS-CoV-2 RNA polymerase structure. Cryo-EM has become a versatile tool in structural biology, capable of producing near-atomic resolution 3D reconstructions. However, the ultimate goal is detailed atomic understanding through atomic models. Raw maps are often not used due to high-resolution contrast loss. Sharpening methods, like LocScale and LocalDeblur, aim to restore contrast but have limitations. DeepEMhancer, a deep learning model, improves map interpretability by learning from data, without requiring atomic models. It outperforms global B-factor methods in resolution and map quality. DeepEMhancer uses a 3D U-net architecture, trained on pairs of experimental and sharpened maps. It improves map resolution and similarity to atomic models, as shown in results. It performs better than global B-factor methods, especially in resolving low-quality regions. DeepEMhancer was tested on maps like EMD-7099 and EMD-4997, showing improved visualization and detail. It also improved the SARS-CoV-2 RNA polymerase map, revealing new residues. DeepEMhancer is a fully automatic deep learning method for cryo-EM post-processing. It improves map quality and resolution, making it more useful for atomic model building. It was tested on 20 maps, showing better results than traditional methods. DeepEMhancer is available as open-source software and is expected to improve with more powerful models and data. It is not a universal solution but can be combined with other techniques for better results. It is important to validate results with ground truth and avoid estimating resolution without it. DeepEMhancer is available for real-world applications, such as improving the SARS-CoV-2 RNA polymerase map.DeepEMhancer is a deep learning approach for cryo-EM volume post-processing. Cryo-EM maps are valuable for protein structure modeling but often require post-processing due to high-frequency contrast loss. Traditional methods like global B-factor correction have limitations, such as ignoring local map quality variations. DeepEMhancer, trained on experimental maps and sharpened maps using atomic models, performs automatic post-processing with masking and sharpening-like operations in one step. It was evaluated on 20 experimental maps, showing improved noise reduction and detailed map versions. DeepEMhancer also enhanced the SARS-CoV-2 RNA polymerase structure. Cryo-EM has become a versatile tool in structural biology, capable of producing near-atomic resolution 3D reconstructions. However, the ultimate goal is detailed atomic understanding through atomic models. Raw maps are often not used due to high-resolution contrast loss. Sharpening methods, like LocScale and LocalDeblur, aim to restore contrast but have limitations. DeepEMhancer, a deep learning model, improves map interpretability by learning from data, without requiring atomic models. It outperforms global B-factor methods in resolution and map quality. DeepEMhancer uses a 3D U-net architecture, trained on pairs of experimental and sharpened maps. It improves map resolution and similarity to atomic models, as shown in results. It performs better than global B-factor methods, especially in resolving low-quality regions. DeepEMhancer was tested on maps like EMD-7099 and EMD-4997, showing improved visualization and detail. It also improved the SARS-CoV-2 RNA polymerase map, revealing new residues. DeepEMhancer is a fully automatic deep learning method for cryo-EM post-processing. It improves map quality and resolution, making it more useful for atomic model building. It was tested on 20 maps, showing better results than traditional methods. DeepEMhancer is available as open-source software and is expected to improve with more powerful models and data. It is not a universal solution but can be combined with other techniques for better results. It is important to validate results with ground truth and avoid estimating resolution without it. DeepEMhancer is available for real-world applications, such as improving the SARS-CoV-2 RNA polymerase map.