This project is a command-line tool designed for image super-resolution and noise reduction, with a primary focus on anime-style illustrations. It utilizes convolutional neural network inference to reconstruct missing pixel data and remove digital artifacts, allowing users to upscale images and reduce noise either independently or in a single simultaneous processing pass.
Beyond its core image restoration capabilities, the software provides a comprehensive suite for machine learning model training. Users can prepare custom datasets and optimize neural networks for specific restoration tasks, supported by a high-performance backend that executes computations on central or graphics processing units. The tool also features automated batch processing, enabling the efficient transformation of large collections of images and video files by applying consistent parameters across entire directories.
The software supports video enhancement by decomposing streams into individual frames for spatial transformation before reassembling them into a final output. While specialized for hand-drawn artwork, it also includes models trained for photographic data to accommodate general imagery. The project is available as a containerized deployment and includes a modern implementation based on the PyTorch framework.
