Struct Epry 

pub struct Epry {

    pub iterations: usize,
    pub object_step: f64,
    pub pupil_step: f64,
    pub batch_size: usize,
    pub recover_pupil: bool,
    pub constrain_pupil_support: bool,
    pub recover_frame_gains: bool,
    pub gain_step: f64,
    pub minimum_gain: f64,
    pub maximum_gain: f64,
    pub recover_background: bool,
    pub background_step: f64,
    pub minimum_background: f64,
    pub maximum_background: f64,
    pub epsilon: f64,
    pub loss_type: LossType,
}

Embedded pupil-recovery reconstruction for Fourier ptychographic microscopy.

Method

EPRY alternates detector-amplitude projection with two normalized updates: the exit-wave error is divided by pupil power to update the overlapping object-spectrum patch and by object-patch power to update the pupil. Jointly recovering these two complex functions lets the pupil absorb aberrations that would otherwise be imprinted on the reconstructed object. The pupil can be projected back onto the known aperture support after every update.

This implementation also optionally estimates a relative gain and an additive, spatially uniform background for each frame. Those calibration updates and incoherent multiplexing support are crate extensions to the reference EPRY method.

Gauge convention

When pupil recovery is enabled, iteration-boundary results use the compiled pupil as a fixed gauge reference. The supported pupil energy is matched to that reference, its supported overlap is made positive real, and the centered object-spectrum coefficient is made non-negative real. Reciprocal object/pupil factors preserve the forward prediction. Affine pupil phase is removed independently on axes whose effective subpixel offsets are zero; it is retained on axes with fractional offsets because the model’s bilinear crop interpolation does not preserve that ambiguity exactly. The runner applies this projection before iteration callbacks, checkpoints, and final result construction, including after checkpoint restoration.

References

X. Ou, G. Zheng, and C. Yang, “Embedded pupil function recovery for Fourier ptychographic microscopy” (2014), Optics Express 22(5), 4960–4972.

The blind object/pupil ambiguities follow A. Fannjiang and P. Chen, “Blind ptychography: uniqueness and ambiguities” (2020), Inverse Problems 36, 045005; this implementation uses a Fourier-domain object and retains affine phase on fractionally interpolated axes.

Fields

iterations: usize

Number of complete passes through the acquisition schedule.

object_step: f64

Relaxation factor applied to each object-spectrum correction.

pupil_step: f64

Relaxation factor applied to each pupil correction.

batch_size: usize

Number of measured frames supplied to each reconstruction step.

recover_pupil: bool

Whether to update the complex pupil alongside the object.

constrain_pupil_support: bool

Whether to zero recovered pupil values outside the compiled aperture.

recover_frame_gains: bool

Whether to estimate one multiplicative intensity gain per frame.

gain_step: f64

Fraction of each least-squares frame-gain estimate applied per update.

minimum_gain: f64

Lower bound for recovered frame gains; must be positive.

maximum_gain: f64

Upper bound for recovered frame gains.

recover_background: bool

Whether to estimate one additive, spatially uniform background per frame.

background_step: f64

Fraction of the mean frame residual added to the background per update.

minimum_background: f64

Lower bound for recovered background intensities.

maximum_background: f64

Upper bound for recovered background intensities.

epsilon: f64

Positive numerical floor used in normalized updates.

loss_type: LossType

Loss used for diagnostics; the projection itself always enforces the measured amplitude.

Implementations

impl Epry

pub fn iterations(self, iterations: usize) -> Self

Sets the number of complete acquisition-schedule passes; validation requires non-zero.

pub fn object_step(self, step: f64) -> Self

Sets the finite positive object-spectrum relaxation.

pub fn pupil_step(self, step: f64) -> Self

Sets the finite positive complex-pupil relaxation.

pub fn recover_pupil(self, recover: bool) -> Self

Enables or disables simultaneous complex-pupil recovery.

pub fn constrain_pupil_support(self, constrain: bool) -> Self

Selects whether pupil values outside the compiled binary support are forced to zero.

pub fn recover_frame_gains(self, recover: bool) -> Self

Enables or disables one positive multiplicative gain estimate per acquisition frame.

pub fn gain_step(self, step: f64) -> Self

Sets the finite gain-estimate relaxation in (0, 1].

pub fn gain_bounds(self, minimum: f64, maximum: f64) -> Self

Sets finite positive inclusive lower and upper bounds for recovered frame gains.

pub fn recover_background(self, recover: bool) -> Self

Enables or disables one spatially uniform additive intensity background per frame.

pub fn background_step(self, step: f64) -> Self

Sets finite background-estimate relaxation in (0, 1].

pub fn background_bounds(self, minimum: f64, maximum: f64) -> Self

Sets finite inclusive bounds for recovered additive intensity backgrounds.

Trait Implementations

impl Clone for Epry

fn clone(&self) -> Epry

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Epry

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for Epry

fn default() -> Self

Returns the “default value” for a type.

impl ReconstructionAlgorithm for Epry

type IterationMetrics = NoIterationMetrics

Algorithm-specific metrics emitted by each step and appended to the trace.

fn validate(&self) -> Result<()>

Validates solver parameters independently of a reconstruction problem.

fn canonicalize_state<M: MeasurementRead>( &self, problem: &ReconstructionProblem<M>, state: &mut ReconstructionState, ) -> Result<()>

Projects algorithm-owned ambiguities into a stable reported convention.

fn step<M: MeasurementRead>( &mut self, problem: &ReconstructionProblem<M>, state: &mut ReconstructionState, batch: &Batch, _iteration: usize, ) -> Result<StepOutput<Self::IterationMetrics>>

Updates state for one scheduled batch in zero-based iteration.

fn iterations(&self) -> usize

Returns the requested number of complete schedule passes.

fn batch_size(&self) -> usize

Returns the number of measured frames combined into one step.

fn validate_problem<M: MeasurementRead>( &self, _problem: &ReconstructionProblem<M>, ) -> Result<()>

Validates solver requirements that depend on problem.

fn initialize<M: MeasurementRead>( &self, problem: &ReconstructionProblem<M>, ) -> Result<ReconstructionState>

Creates the default CPU-backed state for problem.

fn initialize_with_backend<M: MeasurementRead>( &self, problem: &ReconstructionProblem<M>, backend: Arc<dyn Backend>, ) -> Result<ReconstructionState>

Creates reconstruction state using the supplied execution backend.

fn supports_joint_reconstruction(&self) -> bool

Reports whether the algorithm may run inside physical joint calibration.

fn run<M: MeasurementRead>( self, problem: &ReconstructionProblem<M>, ) -> Result<ReconstructionResult>

where Self: Sized,

Runs the algorithm with default sequential scheduling and no callbacks.

fn run_with_callbacks<M: MeasurementRead>( self, problem: &ReconstructionProblem<M>, callbacks: Vec<Box<dyn Callback>>, ) -> Result<ReconstructionResult>

where Self: Sized,

Runs the algorithm and invokes callbacks at their declared hooks.

fn run_from_checkpoint<M: MeasurementRead>( self, problem: &ReconstructionProblem<M>, checkpoint: ReconstructionCheckpoint, ) -> Result<ReconstructionResult>

where Self: Sized,

Resumes a run from a checkpoint after validating it against problem.