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Computational Photography, an AI-powered Slopendium — 38 Revealing the invisible
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▸Part 38 REVEALING THE INVISIBLE⧉
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▸38.1 Accidental cameras⧉
38.1.1 The accidental pinhole: a window is a camera⧉
38.1.2 The accidental pinspeck: the anti-pinhole⧉
38.1.3 The occluder as a crude lens, and recovery as deconvolution⧉
38.1.4 Corners and doorways: an edge that resolves the hidden room⧉
38.1.5 Where else the world hides a camera⧉
▸38.2 Reflections in the eye⧉
fig-world-in-an-eye
fig-world-in-an-eye ·
fig-eyes-for-relighting
fig-eyes-for-relighting ·
38.2.1 The cornea as a catadioptric mirror⧉
38.2.2 The geometry: from a corneal pixel to a direction in the world⧉
38.2.3 What the recovered reflection is good for⧉
▸38.3 Motion and video magnification⧉
fig-eulerian-vs-lagrangian
fig-eulerian-vs-lagrangian ·
38.3.1 The Lagrangian precursor: track, then exaggerate⧉
38.3.2 Eulerian video magnification: amplify the time series at each pixel⧉
38.3.3 Why amplifying brightness amplifies motion⧉
38.3.4 Phase-based magnification: move the motion into phase⧉
38.3.5 What it reveals: vital signs, structures, materials, modes⧉
▸38.4 Visual microphone⧉
38.4.1 From sub-pixel motion to a sound waveform⧉
38.4.2 Bandwidth: high-speed cameras and the rolling-shutter trick⧉
38.4.3 How good is the copy? The object's frequency response⧉
38.4.4 The active cousins: laser vibrometry and interferometry⧉
▸38.5 Corner camera⧉
38.5.1 The edge as a one-dimensional aperture⧉
38.5.2 From a faint gradient to a usable signal⧉
38.5.3 What the corner can and cannot tell you⧉
▸38.6 Active non-line-of-sight⧉
38.6.1 Third-bounce geometry and time-of-flight⧉
38.6.2 The hardware: photographing light in flight⧉
38.6.3 From back-projection to fast, exact inversion⧉
38.6.4 The trade, stated plainly⧉
▸38.7 Passive non-line-of-sight⧉
fig-photoshop-resample
fig-photoshop-resample · the resampling kernel zoo as a shipping product's UI: Photoshop *Image Size* resample-method dropdown screenshot + on-figure legend mapping each menu option to its kernel — Nearest Neighbor→box, Bilinear→tent, Bicubic→cubic, Bicubic Smoother→cubic biased smooth (enlarge), Bicubic Sharper→cubic biased sharp (reduce), Preserve Details 1.0/2.0→edge-aware/learned upsampler. © Adobe Inc., educational commentary ✅
fig-nn-downsample-adversarial
fig-nn-downsample-adversarial · "decimation reveals hidden content": plant single pixels of a 2nd image at the NN sample sites (s·i, s·j) of a base photo — naive NN ÷s reconstructs the **hidden** image, prefiltered (area) ÷s averages it away and keeps the base. Panels: attacked image, pixel-zoom showing the planted dots, hidden reference, NN result, prefiltered result, "why it works". Image-scaling attack — cite arXiv:2104.11222 (Tamkin et al.) & arXiv:2104.08690 (Quiring et al.) 🟨
• **the setup**: no laser, no time-of-flight — only **ambient light** from the hidden scene spilling onto a **relay surface** you can see (a floor, a wall). An **accidental occluder** — a vertical **edge**, a **doorframe**, an opaque object — selectively blocks rays, so different parts of the visible surface "see" different parts of the hidden region. The occluder is what makes the problem solvable: a fully open scene blurs everything together.
• **occluder as a crude lens / coded aperture**: the edge or object **modulates** the hidden light, much like a **pinhole or coded aperture** modulates a normal scene. The light measured on the visible surface is (hidden image) **convolved** with the occluder's transfer/visibility function — so recovering the hidden image is a **deconvolution / inverse problem**, where the **occluder geometry** is itself part of the unknown.
• **corner camera (1-D)**: at a wall corner, the vertical edge acts as a 1-D **angular** aperture — the **penumbra** gradient on the floor encodes a 1-D image of the hidden scene **vs. angle around the corner**; differencing/inverting the penumbra yields a 1-D **video** of motion in the hidden room (Bouman et al. 2017). Cross-ref `[[#Corner camera]]`.
• **computational periscopy (2-D, single photo)**: with an **opaque occluder** of known or estimated shape between the hidden scene and the wall, a **single photograph** of the wall contains enough structure to **invert** for a full **2-D image** of the hidden scene — the occluder's cast shadow plays the role of a coded aperture (Saunders, Murray-Bruce & Goyal 2019).
• **passive vs active — the contrast**: **active** NLOS (above) sends a controlled light pulse and times its return, buying strong signal and depth at the cost of specialized hardware; **passive** NLOS is **photon-starved and ill-conditioned** (faint penumbra signal, unknown occluder), trading robustness for working with **ordinary cameras and existing light** — and it degrades gracefully as the accidental occluder becomes less ideal.
38.7.1 The occluder is what makes it solvable⧉
38.7.2 A deconvolution where the lens is unknown⧉
38.7.3 One dimension: the corner camera⧉
38.7.4 Two dimensions from a single photo: computational periscopy⧉
38.7.5 Active versus passive, the ledger⧉
▸38.8 Mm-wave, wifi⧉
38.8.1 Why radio walks through walls⧉
38.8.2 Time of flight, again — radar is NLOS with a longer wave⧉
38.8.3 From a radio smear to a human skeleton: the learned map⧉
38.8.4 What it sees, and what it costs us⧉