A New Benchmark and Baseline for Real-Time High-Resolution Image Inpainting on Edge Devices

Marcelo Sánchez Ortega, Gil Triginer Garces, Coloma Ballester, Ignacio Sarasua, Lara Raad; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1133-1143

Existing image inpainting methods have shown impressive completion results for low-resolution images. However most of these algorithms fail at high resolutions and require powerful hardware limiting their deployment on edge devices. Motivated by this we propose the first baseline for REal-Time High-resolution image INpainting on Edge Devices (RETHINED) that is able to inpaint at ultra-high-resolution and can run in real-time (30ms) in a wide variety of mobile devices. A simple yet effective novel method formed by a lightweight Convolutional Neural Network (CNN) to recover structure followed by a resolution-agnostic patch replacement mechanism to provide detailed texture. Specially our pipeline leverages the structural capacity of CNN and the high-level detail of patch-based methods which is a key component for high-resolution image inpainting. To demonstrate the real application of our method we conduct an extensive analysis on various mobile-friendly devices and demonstrate similar inpainting performance while being 100 times faster than existing state-of-the-art methods. Furthemore we release DF8K-Inpainting the first free-from mask UHD inpainting dataset.