Image upscaling is the process of enlarging and enhancing low-resolution images using various algorithms. There are many FOSS image upscaling algorithms and software available, but their quality and performance may vary depending on the type and complexity of the input images.

Based on the search results, some of the best FOSS image upscaling algorithms and software for photorealistic images are:

Upscayl: A cross-platform application that uses Real-ESRGAN, an advanced AI algorithm that can upscale images without losing quality. It has a user-friendly GUI and supports Linux, MacOS and Windows. It claims to have over 1 million downloads.
waifu2x: A popular algorithm that was originally designed for upscaling anime-style images, but can also work well for photorealistic images. It uses deep convolutional neural networks to enhance the details and reduce noise. It has many implementations in different languages and platforms, such as waifu2x-GUI.
Neural Image Super Resolution (NISR): A collection of Colab notebooks that use various neural network models to enhance low-resolution images. It supports models such as ISR, ESRGAN and Latent Diffusion.
BasicSR: A Python library that includes ESRGAN and SFT-GAN for training and testing super-resolution models. It can upscale images with different styles and domains.
SwinIR: A state-of-the-art image super-resolution model that uses Swin Transformer, a novel self-attention mechanism. It can handle various degradation scenarios and achieve high-quality results. It can be run in JavaScript/Wasm using an ONNX-ported version.
These are some of the best FOSS image upscaling algorithms and software for photorealistic images, according to the search results. However, you may want to try them yourself and see which one suits your needs best.
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