decompose op• 데이터 종류: image2d → table
• 호출: import fullseye as fs; fs.ledger.complex_steerable_decompose(image, scales: 'int' = 4, orientations: 'int' = 4) -> 'dict'(구현을 직접 호출하려면 import motionmag; motionmag.complex_steerable_decompose(image, scales: 'int' = 4, orientations: 'int' = 4) -> 'dict', 원장에서 가져오려면 opsmotionmag.get("complex_steerable_decompose"))
한 프레임의 복소 방향 선택적 서브밴드 분해 -> dict.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Splits the image into scales * orientations analytic sub-bands plus three residuals (low-pass, high-pass and a small symmetric completion band). Each sub-band is a full-resolution (H, W) complex array whose modulus is the local contrast of that scale/orientation and whose argument is the local phase — the quantity a translation shifts linearly, which is what the rest of this module is built on. There is no spatial decimation: keeping every band at full resolution costs memory but makes the frame exactly invertible, and exactness is the point.
Returns {"bands": [complex (H, W), ...], "kinds": [...], "centre_cycles_per_px": [...], "orientation_rad": [...], "shape": (H, W), "scales": s, "orientations": k}. kinds[j] is "band" for an oriented sub-band and "lowpass" / "highpass" / "residual" otherwise; centre_cycles_per_px and orientation_rad are None for residuals, which have no orientation and no single centre frequency.
Feed the whole dict back to complex_steerable_reconstruct. Round trip error, measured on a 64x64 random frame with the defaults, is max|out - in| = 6.66e-16; on a 31x37 (odd, non-square) frame 7.22e-16; and the worst over every scales in 1..8 crossed with every orientations in 1..16 on 32x32 is 7.77e-16 — the tight-frame construction is exact, not approximate (see the notes on the self-conjugate grid points
in _filter_bank).
References: Freeman & Adelson, IEEE PAMI 1991; Simoncelli & Freeman, ICIP 1995; Portilla & Simoncelli, IJCV 2000.
• motion_magnification 패밀리 가이드
• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.
• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.
• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.
• motion_magnification — py -3.11 examples/motion_magnification.py
table 를 입력으로 받는 것)decompose)*Provenance: motionmag.py — MOTIONMAG 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.