advanced28 min studyLesson 227 in Physics

Intensity data leave phase ambiguities

Identify what Fourier intensity determines and what reconstruction constraints must add.

On this page

What you’ll understand

  • Derive the autocorrelation determined by Fourier intensity.
  • Exhibit multiple objects with identical measured intensities.
  • Distinguish data consistency, uniqueness, and algorithmic convergence.

A far-field diffraction pattern measures the squared magnitude of a Fourier transform. It does not label the transform phase at each detector position. Phase retrieval asks whether additional information, such as finite support or several propagation planes, can recover a useful object. The central difficulty is not merely finding a clever inverse transform: distinct objects can produce identical data. We will prove a small ambiguity before examining what iterative constraints can and cannot establish.

What the detector actually supplies

Let a coherent object field be u and its Fourier transform be U, using the cycles-per-length convention. The measured far-field intensity is proportional to the squared modulus of U after known propagation factors are removed. Taking the inverse Fourier transform of this intensity produces the autocorrelation of u. Thus the data contain pairwise difference information. They are not equivalent to direct access to the complex Fourier transform, whose inverse would immediately supply the field.

Autocorrelation connects an object with a conjugated shifted copy of itself, integrating over their overlap. It can identify support extents and repeated separations, yet it does not assign those separations uniquely to individual features. With finite sampled data one must distinguish circular DFT autocorrelation from the aperiodic correlation of a compact object padded with zeros. Insufficient padding mixes opposite boundaries. A reconstruction that ignores this distinction can match its own numerical model while misinterpreting the physical experiment.

Y(f)=∣U(f)∣2,F−1[Y](s)=∫u(x+s)u∗(x) dxY(f)=|U(f)|^2,\qquad \mathcal F^{-1}[Y](s)=\int u(x+s)u^*(x)\,dx
u is the complex object field and U(f)=∫u(x)e^(−i2πfx)dx. The inverse transform gives an aperiodic autocorrelation for the continuous ideal model; sampling requires its own boundary convention.↔ Scroll wide equations horizontally.

Prove ambiguities before claiming uniqueness

Multiplying the whole object by one constant unit-modulus phase rotates every Fourier coefficient but leaves all intensities unchanged. Translating the object multiplies its transform by a frequency-dependent unit-modulus phase and also leaves the intensity unchanged. Conjugating and reversing the object produces the conjugate transform, again preserving the measured magnitude. A support condition or an external position reference may rule out some translations, but an intensity-only experiment does not determine a global field phase.

These familiar ambiguities are not the entire uniqueness problem. One-dimensional Fourier magnitude can have further nontrivial ambiguities even with finite support. Higher-dimensional support, positivity, oversampling, masks, or diversity measurements can improve identifiability under explicit assumptions, but no universal recovery guarantee follows from the word oversampling alone. An opaque or complex-valued specimen may violate nonnegativity assumptions used successfully for other objects. A prior is useful only when its physical justification is stated.

F[eiϕ0u(x−x0)]=eiϕ0e−i2πfx0U(f),F[u∗(−x)]=U∗(f)\mathcal F[e^{i\phi_0}u(x-x_0)]=e^{i\phi_0}e^{-i2\pi f x_0}U(f),\qquad \mathcal F[u^*(-x)]=U^*(f)
φ₀ is a constant phase in radians and x₀ is a translation in metres. Both right-hand multipliers or conjugation preserve |U|, demonstrating intensity-data symmetries.↔ Scroll wide equations horizontally.

Iteration enforces constraints without creating measurements

A basic alternating procedure Fourier transforms an object estimate, replaces its modulus by the measured modulus while retaining its current phase, inverse transforms, then enforces object-domain constraints. Those constraints might set values outside a known support to zero or impose a justified real nonnegative object. The sets are generally nonconvex, so iterates can stagnate or settle at different consistent solutions. A decreasing residual demonstrates improved agreement with the specified data model, not proof that a unique physical object has been found.

The vectors instrument isolates magnitude-only information. Reset and set both controls to angle zero degrees and magnitude one; predict components one and zero. Reset again and set angle 90 degrees and magnitude one; predict components zero and one. The magnitude stays fixed while the vector changes. This is an analogy for one complex coefficient with missing phase. The instrument displays both components and does not perform Fourier propagation, impose support, or run a phase-retrieval algorithm.

Two positive objects fit the same data

A two-sample real nonnegative object has unnormalized DFT coefficients U₀=x+y and U₁=x−y. Measured intensities are 9 and 1. Find the two possible ordered objects with x+y positive, and show that both reproduce the data.

  1. The zero-frequency intensity gives (x+y)²=9. Nonnegativity selects x+y=3 rather than minus three, which fixes the total object amplitude.

  2. The other measurement gives (x−y)²=1, so x−y can be either plus one or minus one. The detector has not measured this sign.

  3. Solving the two linear systems gives (x,y)=(2,1) or (1,2). Their DFTs are respectively (3,1) and (3,−1), whose squared magnitudes are both (9,1).

The result

Both ordered objects (2,1) and (1,2) satisfy nonnegativity and exactly fit the intensity data; the measurement cannot distinguish them.

Explore the model

Interactive lab

Interactive lab · vectors

A fixed magnitude leaves a phase circle

Reset and set BOTH controls to angle 0° and magnitude 1, predicting components (1,0). Reset and set angle 90° and magnitude 1, predicting (0,1). The equal magnitudes illustrate one missing phase, not a reconstruction algorithm.

A fixed magnitude leaves a phase circlePlayback traces a fixed vector and its projections. Angle and magnitude set the geometry; the moving marker does not change them. Current readings: x component: 1.393; y component: 0.975; Magnitude: 1.7. All parameters are adjustable with the labeled controls below.−7.46−3.7303.737.46−3.5−1.7501.753.5xyvvₓ = 1.39
Vectorx / y projectionsTrace marker
x component
1.393
y component
0.975
Magnitude
1.7
t = 0 s

Playback traces a fixed vector and its projections. Angle and magnitude set the geometry; the moving marker does not change them. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.

Look forA magnitude measurement alone cannot distinguish the different complex directions around its circle.

Check your understanding

What is established when an iterative phase-retrieval estimate exactly reproduces all measured intensities?

Try it yourself

Take a three-sample object (1,0,0) and its cyclic shift (0,1,0). Using the unnormalized three-point DFT, compare their three intensity values. Explain what extra reference would distinguish their positions.

Take a moment to make a prediction or work it out on paper. Open a hint when you need one.

Give me a hint

The first transform is constant one. A cyclic shift multiplies it by exp(−i2πk/3).

Compare with the solution

The first transform is (1,1,1). The shifted transform is (1,exp(−i2π/3),exp(−i4π/3)); all entries have modulus one, so both intensity vectors are (1,1,1). A calibrated real-space location measurement or interference with a known position reference can distinguish the shift. The Fourier intensity alone cannot.

Take it with you

  • Fourier intensity determines autocorrelation, with a sampling-dependent boundary convention.
  • Global phase, translation, and conjugate inversion create familiar ambiguities.
  • A small residual does not certify unique recovery.

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