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arXiv Machine Learning By Tayyab Nasir, Daochang Liu, Ajmal Mian

WAVE: Reversing the Guidance Hierarchy for Coarse-to-Fine Guided Depth Super-Resolution

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WAVE introduces a multi-level discrete wavelet transform (ML‑DWT) to reverse the typical fine‑to‑coarse bias in guided depth super‑resolution. By consuming wavelet sub‑bands and semantic tokens in reverse order, it separates structure and detail reconstruction, applies semantic gating to high‑frequency bands, and fuses modalities via an invertible coupling mechanism. Experiments on multiple benchmarks show that WAVE matches or outperforms existing methods, especially at high upsampling factors where low‑resolution depth has minimal structure.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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