MISR-GS: Test-Time Multi-Image Super-Resolution with Explicit 2D Gaussian Fields

Multi-image super-resolution reconstructs a high-resolution image from multiple low-resolution observations captured with sub-pixel misalignments. Recent test-time optimization methods address this problem by representing the unknown high-resolution signal as a shared implicit neural field and jointly optimizing frame-wise alignment. However, coordinate-based implicit representations rely on global MLPs and frequency-based positional encodings, which may limit local capacity allocation and introduce domain-sensitive hyperparameters. We propose MISR-GS, a test-time multi-image super-resolution framework based on explicit 2D Gaussian fields. MISR-GS represents the unknown high-resolution image as a set of learnable anisotropic 2D Gaussian primitives shared across all low-resolution frames. Each frame is reconstructed through a differentiable render-and-degrade process, where the shared Gaussian field is rendered at the target resolution, warped by a frame-specific affine transformation, photometrically corrected, and downsampled to match the corresponding low-resolution observation. To adapt Gaussian representations to this inverse problem, we introduce observation-driven initialization and multi-frame residual-guided densification, both derived solely from low-resolution inputs. Experiments on synthetic and real burst datasets demonstrate the effectiveness, efficiency, and robustness of MISR-GS for test-time multi-image super-resolution.