watermedia op• Data kinds: rgbimage × image2d × image2d → table
• Call: import fullseye as fs; fs.ledger.underwater_image(image, depth_map_m, distance_map_m, water_type='jerlov_I', wavelengths_um=None, illumination='daylight', backscatter=True, apply_ambient=False, beta_b_ratio=1.0, backscatter_ratio=0.02) -> 'dict' (to call the implementation directly, import watermedia; watermedia.underwater_image(image, depth_map_m, distance_map_m, water_type='jerlov_I', wavelengths_um=None, illumination='daylight', backscatter=True, apply_ambient=False, beta_b_ratio=1.0, backscatter_ratio=0.02) -> 'dict'; from the registry, opsoptics.get("underwater_image"))
• GPU: none (CPU reference implementation only)
The image of a scene recorded through water (revised Jaffe-McGlamery model) -> dict.
Following Akkaynak & Treibitz (2018) the recorded image is I = J exp(-beta_D r) + B_inf (1 - exp(-beta_B r)): the direct signal J of the object attenuated along the camera-object distance r with a wavelength-dependent coefficient, plus backscatter, the veiling light B_inf that builds up along the same path with its own coefficient. image is (H, W, K), non-negative: the appearance of the object at zero distance, per band (K = 3 is RGB with the effective wavelengths 0.61 / 0.54 / 0.465 um, other K need wavelengths_um). depth_map_m is the depth of the scene below the surface and distance_map_m the distance to the camera, each (H, W) or a scalar. beta_D is the diffuse attenuation Kd of water_attenuation (used as the attenuation along the line of sight, an approximation that Akkaynak & Treibitz show to depend on range) and beta_B = beta_b_ratio beta_D. The veiling light is the single-scattering value B_inf = E(z) b_b / (2 c) with b_b = backscatter_ratio b and E(z) = E_0 exp(-Kd z) the daylight left at the depth (an assumption, not a measurement). illumination is "daylight" (flat relative spectrum 1) or a (K,) relative spectrum at the surface. With apply_ambient the object is also dimmed by the ambient light at its depth, J = image E(z); without it image is taken as it already is at the object, so that distance 0 returns it exactly. backscatter=False leaves pure Beer-Lambert. The table has image (the recording), direct, backscatter, transmission (exp(-beta_D r)), beta_D, beta_B, veiling_light (H, W, K), ambient (E(z)/E_0), wavelengths_um and, for RGB, red_to_blue_kd_ratio. Non-finite or negative inputs are refused.
Every optics op validates its input before computing (nothing slips through silently):
• Units are baked into the argument name — _mm / _um / _deg / _mrad. Confusing mm with µm does not crash; it yields a plausible-looking wrong answer, so the name prevents it. Nothing here guesses the unit from the magnitude.
• Strings raise ValueError — float('50') succeeds, so an unparsed configuration value would slip through as a length (measured: thin_lens('50', '200') returned a plausible 66.667 mm). bool is refused too, as the implicit promotion True == 1.
• complex / masked arrays raise ValueError (real-valued slots only; silently dropping the imaginary part or peeling off the mask is refused). NaN/Inf raises ValueError on every input.
• Division by zero and its relatives are refused by name: focal length 0, radius of curvature 0, refractive index <= 0, a fully opaque aperture (all zeros, so the normalisation is 0/0), a PSF whose sum is <= 0, a Stokes vector with S0 = 0, and an object sitting at the front focal point (the image is at infinity).
• Only two ops return a non-finite value, and both state it as a contract: depth_of_field returns far_mm = inf beyond the hyperfocal distance (that is what the hyperfocal distance means), and gaussian_beam returns wavefront_radius_mm = inf at the waist (the radius of curvature of a plane wavefront). Both also return a finite companion (far_is_infinite / curvature_per_mm). Any other silent NaN/Inf is detected internally and raises ValueError — "float64 overflowed" and "the answer is infinite" are different claims, so the first is never returned wearing the face of the second.
• Size caps: generated grids are capped by optics.MAX_GRID (4096); supplied fields/PSFs/apertures by optics.MAX_FIELD_ELEMENTS (2^24); ABCD element chains by optics.MAX_SYSTEM_ELEMENTS (1024); Zernike by MAX_ZERNIKE_TERMS (512) / MAX_ZERNIKE_ORDER (40) / MAX_ZERNIKE_BASIS (2^25). This closes, fail-closed, the paths where a small argument triggers a huge internal allocation (measured: n_max=40 × 4096² needs 108 GB).
• Physically impossible states are refused too: a Stokes vector with degree of polarisation > 1, negative transmittance, negative intensity, and invalid Zernike indices such as n-|m| odd.
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_water_transparent_media — py -3.11 examples/poc_water_transparent_media.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
watermedia)ocean_spectrum · ocean_height_field · fresnel_water_air · snell_window · refracted_view · water_attenuation · phase_function_hg · scattering_phase_petzold
*Provenance: watermedia.py — OPTICS operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.