The paper presents a novel approach called Content-Aware Image Restoration (CARE) that leverages deep learning to enhance fluorescence microscopy images. CARE addresses the limitations of traditional microscopy, such as low signal-to-noise ratios, high light exposure, and reduced imaging depth, by restoring images to higher quality with fewer photons. The method is demonstrated on seven specific examples, including the imaging of *Schmidtea mediterranea* flatworms, *Tribolium castaneum* beetle embryos, and Drosophila melanogaster embryos. CARE shows significant improvements in image quality, segmentation accuracy, and tracking precision, enabling longer and faster imaging sessions. The approach also allows for isotropic resolution in 3D volumes and the resolution of sub-diffraction structures at high frame rates. The CARE networks are trained using a combination of physically acquired and semi-synthetic training data, and the results are validated through various metrics and comparisons with classical methods. The availability of CARE networks and associated software tools makes this method accessible to the scientific community, potentially revolutionizing biological image analysis.The paper presents a novel approach called Content-Aware Image Restoration (CARE) that leverages deep learning to enhance fluorescence microscopy images. CARE addresses the limitations of traditional microscopy, such as low signal-to-noise ratios, high light exposure, and reduced imaging depth, by restoring images to higher quality with fewer photons. The method is demonstrated on seven specific examples, including the imaging of *Schmidtea mediterranea* flatworms, *Tribolium castaneum* beetle embryos, and Drosophila melanogaster embryos. CARE shows significant improvements in image quality, segmentation accuracy, and tracking precision, enabling longer and faster imaging sessions. The approach also allows for isotropic resolution in 3D volumes and the resolution of sub-diffraction structures at high frame rates. The CARE networks are trained using a combination of physically acquired and semi-synthetic training data, and the results are validated through various metrics and comparisons with classical methods. The availability of CARE networks and associated software tools makes this method accessible to the scientific community, potentially revolutionizing biological image analysis.