PBSR: Pseudo-Burst Super-Resolution via Zero-Shot Novel View Synthesis
Yuki Kondo, Norimichi Ukita
Abstract
Burst super-resolution (BSR) achieves higher reconstruction fidelity than single-image super-resolution (SISR) by aggregating sub-pixel information from multiple frames. However, it requires a burst of frames captured under near-static conditions, and such frames are unavailable for single photographs or archival images. We propose Pseudo-Burst Super-Resolution (PBSR), which brings BSR-style reconstruction to the single-image input setting by synthesizing pseudo-burst frames via zero-shot novel view synthesis (NVS). PBSR first generates pseudo-burst frames with realistic parallax using a viewpoint sampling strategy calibrated to the pixel-shift distribution of real burst datasets. The frames are then processed by a BSR backbone equipped with a geometry-aware alignment module that reuses the NVS camera parameters and estimated depth. While the NVS stage is zero-shot, the backbone is fine-tuned with an artifact-aware adaptation so that it can exploit the synthesized frames despite their generative artifacts. Experiments on the BurstSR dataset show that PBSR consistently improves in PSNR, SSIM, and LPIPS over the single-frame baseline with the same reconstruction backbone.