GRAPE (Gaussian Rendering for Accelerated Pixel Enhancement) Brings Fast and Lightweight Arbitrary Super-Resolution

Jung In Jang, Kyong Hwan Jin; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026, pp. 7750-7758

We present GRAPE--Gaussian Rendering for Accelerated Pixel Enhancement, a fast, lightweight method for arbitrary-scale super-resolution (ASSR) based on 2D Gaussian splatting. Lookup-table (LUT) schemes are limited to preset scale factors and struggle with varied textures, while implicit neural representations (INRs) slow down because they require per-coordinate queries; moreover, prior Gaussian-splatting approaches rely on heavy networks or complex processing. GRAPE overcomes these limitations with a compact design in which a single point-wise layer predicts anisotropic Gaussian parameters--RGB value, rotation, scale, and offset--and a differentiable rasterizer then renders the high-resolution image in one pass. The entire model, including both encoder and decoder, contains just 1.56 M parameters and requires only 1.10 GB of GPU memory, yet achieves 69.33 FPS on Urban100 at x4 whose average image size is 985 x 798. This is more than 315x faster than GSASR, a 20.45 M-parameter model that runs at 0.22 FPS. Although GRAPE does not further improve perceptual fidelity over heavier networks, it remains competitively close, providing an attractive quality-efficiency trade-off across Set5, Set14, BSD100, DIV2K and Urban100. Consequently, GRAPE is ideal for resource-limited deployments or interactive applications that require rapid screen updates. The source code will be made publicly available at github.com/mulkkog/GRAPE.