arXiv:2609.13409v1 Announce Type: cross
Abstract: Phase unwrapping is a key step in interferometric and coherent imaging, where the physical quantity of interest is carried by a phase that the instru...
By Antoine Moevus, Max Mignotte
arXiv:2606. 08132v1 Announce Type: cross Abstract: Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can change the token evidence available to a pixel, especially near boundaries.
By O\u{g}uzhan Ercan
Genesis is a generative engine that produces fully consistent multi‑scale satellite image pyramids by combining a vertical super‑resolution model with a horizontal mask‑based outpainting model. It addresses the lack of existing methods that can synthesize a complete quadtree from sparse seed tiles at arbitrary zoom levels and positions, ensuring coherence across both scale and space. The authors also release dense500, a comprehensive multi‑scale dataset and evaluation suite, to benchmark this new task.
By Subash Khanal, Yangzhi Cui, Daniel Cher, Eric Xing, Brian Wei, Srikumar Sastry, Nathan Jacobs
Genesis is a generative engine designed to synthesize complete, globally consistent quadtree pyramids for satellite imagery. It tackles the new multi‑scale tile completion task by combining a vertical super‑resolution model with a horizontal mask‑based outpainting model, enabling seamless generation across arbitrary zoom levels and positions. The authors also release dense500, a fully observed multi‑scale dataset, and a suite of pyramid‑level metrics to benchmark performance.
arXiv:2602.19736v3 Announce Type: replace
Abstract: Diffusion models now give the best perceptual quality in super-resolution (SR), but their architecture and training confine them to small fixed cro...
By Shoukun Sun, Zhe Wang, Xiang Que, Jiyin Zhang, Xiaogang Ma
arXiv:2607. 18990v1 Announce Type: cross Abstract: SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distributions from which solutions are sampled at inference time.
By Federico Carrara, Aman Kukde, Melisande Croft, Joran Deschamps, Florian Jug