AI Editing Tools for Photographers
The best AI-powered tools for professional photo editing, background removal, upscaling, and portrait retouching — all in this collection. Discover 6 tools including Luminar Neo, Topaz Photo AI, Remove.bg, Photoroom, and Let's Enhance alongside open-source models like Real-ESRGAN and GFPGAN. Curated for studio photographers, wedding photographers, e-commerce sellers, and content creators, this collection covers advanced capabilities such as RAW editing, batch processing, noise reduction, and AI sky replacement.
Tools
Models
Real-ESRGAN
Real-ESRGAN is an open-source image upscaling and restoration model developed by Xintao Wang and collaborators at Tencent ARC Lab that enhances low-resolution, degraded, or compressed images to high-resolution outputs with remarkable detail recovery. Released in 2021 under the BSD license, Real-ESRGAN builds on the original ESRGAN architecture by introducing a high-order degradation modeling approach that simulates the complex, unpredictable quality loss found in real-world images, including compression artifacts, noise, blur, and downsampling. The model uses a U-Net architecture with Residual-in-Residual Dense Blocks as its generator network, trained with a combination of perceptual loss, GAN loss, and pixel loss to produce sharp, natural-looking upscaled results. Real-ESRGAN supports upscaling factors of 2x, 4x, and higher, and includes specialized model variants for anime and illustration content alongside the general-purpose photographic model. The model handles real-world degradations far better than its predecessor ESRGAN, which was trained only on synthetic degradation patterns. Real-ESRGAN has become one of the most widely deployed AI upscaling solutions, integrated into numerous applications including desktop tools, web services, mobile apps, and professional image editing workflows. The model runs efficiently on both CPU and GPU, with the lighter RealESRGAN-x4plus-anime variant optimized for consumer hardware. As a fully open-source project available on GitHub with pre-trained weights, it serves as the backbone for popular tools like Upscayl and various ComfyUI nodes. Real-ESRGAN is essential for photographers, content creators, game developers, and anyone who needs to enhance image resolution while preserving natural appearance and adding realistic detail.
GFPGAN
GFPGAN is a practical face restoration algorithm developed by Tencent ARC that leverages generative facial priors embedded in a pre-trained StyleGAN2 model to restore severely degraded face images with remarkable quality. First released in December 2021, GFPGAN addresses the challenging problem of blind face restoration where input images may suffer from unknown combinations of low resolution, blur, noise, compression artifacts, and other forms of degradation. The model's architecture combines a degradation removal module with a StyleGAN2-based generative prior, using a novel channel-split spatial feature transform layer that balances fidelity to the original face with the high-quality facial details provided by the generative model. This approach allows GFPGAN to restore fine facial details including skin textures, eye clarity, hair strands, and tooth definition that are completely lost in the degraded input. The model processes faces through a U-Net encoder that extracts multi-resolution features from the degraded image, which then modulate the StyleGAN2 decoder's feature maps to produce a restored output that preserves the original identity while dramatically enhancing quality. GFPGAN excels in old photo restoration, enhancing low-resolution surveillance footage, improving video call quality, recovering damaged family photographs, and preparing low-quality source material for professional use. The model is open source under Apache 2.0, available on Hugging Face and Replicate, and has become a foundational component integrated into numerous creative AI tools and pipelines. Its ability to handle real-world degradation patterns rather than just synthetic corruption makes it particularly valuable for practical restoration tasks encountered by photographers, archivists, and content creators.