{"slug": "neural-phase-correlation", "title": "Neural Phase Correlation", "summary": "Researchers introduced a learned generalization of phase correlation that lifts the restriction of fixed basis, enabling direct measurement of inter-image relationships in the Fourier domain for dense non-rigid deformations and unitary dynamics. The framework matched or exceeded prior baselines on ACDC cardiac-MRI and CAMUS echocardiography benchmarks, and recovered eigenstates and energy levels of the 1-D quantum harmonic oscillator from observation pairs alone.", "body_md": "arXiv:2606.18496v1 Announce Type: new\nAbstract: Correspondence is fundamentally relational: it seeks the unknown transformation between two observations of a common scene, not the content of either. Yet the dominant learning-based methods do not represent the transformation as a first-class object in the architecture. They encode each image independently and let a learned similarity function or a deep decoder discover the mapping implicitly. Phase correlation is the canonical exception, measuring the inter-image relationship directly in the Fourier domain, but the rigidity of its fixed basis confines it to global translation.\nWe introduce a learned generalization of phase correlation that lifts this restriction by learning the basis on which the transformation decomposes. The same algebraic primitive extends to dense non-rigid deformations and to unitary dynamics. On the ACDC cardiac-MRI benchmark the framework matches or exceeds prior published baselines on both registration directions. On CAMUS echocardiography it matches state-of-the-art without auxiliary scoring or adaptive-smoothness mechanisms. Applied to time-evolved wavefunction pairs of the 1-D quantum harmonic oscillator, the same framework recovers the Hermite-function eigenstates and the quantized energy levels of the unknown Hamiltonian from observation pairs alone.", "url": "https://wpnews.pro/news/neural-phase-correlation", "canonical_source": "https://arxiv.org/abs/2606.18496", "published_at": "2026-06-18 04:00:00+00:00", "updated_at": "2026-06-19 02:00:57.172042+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "ai-research"], "entities": ["ACDC", "CAMUS"], "alternates": {"html": "https://wpnews.pro/news/neural-phase-correlation", "markdown": "https://wpnews.pro/news/neural-phase-correlation.md", "text": "https://wpnews.pro/news/neural-phase-correlation.txt", "jsonld": "https://wpnews.pro/news/neural-phase-correlation.jsonld"}}