Optics-Informed Intensity Adjustment (OIA)

• OIA is a family of methods that explicitly controls and corrects intensity using measured or modeled optical priors across various optical systems.
• It utilizes mechanisms such as pupil-plane SLMs, calibrated transfer functions, diffractive processors, and AI-driven modules for compensating non-uniformities and energy errors.
• Demonstrated improvements include enhanced pupil uniformity and higher Strehl ratios, underscoring OIA’s utility where phase-only corrections fall short.

Optics-informed Intensity Adjustment (OIA) denotes a family of optical methods in which intensity is treated as an explicit object of control, calibration, inversion, or learned correction, rather than as a secondary consequence of phase manipulation alone. In published work from 2019 to 2025, the label encompasses pupil-plane intensity correction in adaptive optics, spectrally calibrated microscopy correction, intensity-corrected light-in-flight reconstruction, AI-driven contrast tuning in OCT-SLO, universal linear intensity transformations under spatially-incoherent illumination, longitudinal focal shaping in high-intensity optics, and optics-driven neural feature adjustment for metalens endoscopy. Taken together, these works suggest a common principle: measured or modelled optical priors are used to compensate spatial non-uniformity, total energy error, spectral imbalance, or application-specific intensity distortions (Zhao et al., 2024, Morland et al., 2021, Platonova et al., 2019, Goswami, 2024, Rahman et al., 2023, Oubrerie et al., 2021, Blum et al., 27 Sep 2025, Li et al., 5 Aug 2025).

1. Conceptual scope and recurring structure

Across the literature, OIA does not denote a single algorithm. Rather, it appears as a recurring strategy in which the optical system is endowed with an intensity model, an intensity sensor or proxy metric, and a compensator or inversion rule. In some cases, the compensator is a pixelated intensity corrector in a pupil-conjugate plane; in others it is a per-pixel calibration curve, a diffractive processor, a variable-focus liquid lens and reference-arm sweep, an aspheric mirror sag profile, or a learned attention module. The shared feature is that the correction is informed by optical quantities such as intensity distribution, total energy, spectral throughput, Rayleigh scattering, focusing geometry, point-spread-function structure, or wavelength-dependent efficiency.

Setting	Adjusted quantity	Representative mechanism
Adaptive optics	non-uniform intensity distribution and net energy loss at the pupil plane	pixelated SLM with crossed polarizers and dual-feedback loop
Light-in-flight imaging	optics- and atmosphere-modulated SPAD intensity	forward model and per-pixel correction factor
Bright-field microscopy	raw counts to photon-proportional flux	spectral transfer function and per-pixel calibration curves
OCT-SLO	sample specific automatic contrast adjustment of the beam	AI scoring over liquid-lens voltage and reference-arm length
Diffractive processing	arbitrary linear transformation in time-averaged intensity	phase-only diffractive layers under incoherent illumination
Metalens endoscopy	intensity decay and radial vignetting	optical embeddings and channel/spatial attention

This breadth is a source of both utility and terminological ambiguity. A common misconception is to treat OIA as synonymous with amplitude flattening in adaptive optics. The published record is broader: some OIA methods are closed-loop control schemes, some are inverse physical models, some are fabrication-time optical designs, and some are end-to-end learned modules embedded in larger computational imaging systems.

2. Pupil-plane intensity control in adaptive optics

The most explicit formalization of OIA appears in "Intensity adaptive optics" (Zhao et al., 2024), which introduces intensity adaptive optics (I-AO) as an adaptive-optics framework that corrects not only phase and polarisation but also spatial and total energy errors in an optical system. I-AO treats the pupil-plane intensity map as a controlled degree of freedom by inserting, in conjugate to the system pupil, a pixelated intensity corrector implemented as an SLM between crossed polarizers, together with a two-stage feedback controller. The first loop addresses non-uniform intensity distribution; the second compensates for energy loss at the pupil plane.

The intensity-error model is written as

$$I_{\mathrm{err}}(x,y)=T(x,y)\,I_{\mathrm{in}}(x,y), \qquad 0 \le T(x,y) \le 1.$$

Using the SLM as a controllable retarder, the pixel transmittance is

$T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$

The corrected distribution is chosen so that

$$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$

which yields the pixel-wise retardance pattern

$$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$

After the first loop, the residual net energy error is

$$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$

and the second loop sets the attenuator according to

$$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$

Two implementation pathways are given. In the sensor-based pathway, a pupil-conjugate camera measures $I_{\mathrm{err}}(x,y)$, the SLM applies $\delta^*(x,y)$, and a deformable mirror then runs a standard phase AO routine to remove residual $\phi(x,y)$. In the sensorless pathway, the system sequentially applies intensity Zernike modes $Z_n(x,y)$ with trial amplitudes $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$0, evaluates focal-plane image quality, and maximizes

$T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$1

before a final DM-based phase correction.

Performance is quantified by the pupil-plane uniformity metric

$T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$2

and the Strehl ratio

$T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$3

Representative results are reported as follows: without I-AO, $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$4 and $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$5; sensor-based I-AO after SB1 yields $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$6, $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$7, and after SB2 yields $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$8, $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$9; sensorless I-AO after SL1 yields $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$0, $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$1, and after SL2 yields $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$2, $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$3. These improvements, described as an $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$4–$$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$5 increase in $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$6 and $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$7 in $$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$8, are used to argue that conventional phase-only AO cannot recover either spatial uniformity or absolute energy when intensity errors dominate.

3. Physics-based inversion and calibration workflows

A second major strand of OIA uses explicit forward models and calibrated transfer functions to demodulate measured intensities. In "Intensity-corrected 4D light-in-flight imaging" (Morland et al., 2021), the recorded SPAD intensity for a laser pulse propagating in air is modeled as

$$I_{\mathrm{corr}}(x,y)=T(x,y)\,T_{\mathrm{SLM}}(x,y)\,I_{\mathrm{in}}(x,y)\simeq I_{\mathrm{ref}}(x,y),$$9

where the factors account for geometric or optical focusing, Rayleigh scattering, and finite integration length along the path subtended by one pixel. The inversion fits the measured amplitudes $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$0 and arrival times $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$1 to the intensity model and time-delay model, computes a per-pixel correction factor

$$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$2

and recovers the true intensity as

$$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$3

For pulses traveling toward the camera at nominal $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$4, the joint fit returned $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$5 with residual RMS error on the intensity map $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$6; for pulses traveling away at $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$7, the fit returned $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$8 with intensity residuals again $$\delta^*(x,y)=2\,\arcsin\!\left[\sqrt{I_{\mathrm{ref}}(x,y)/I_{\mathrm{err}}(x,y)}\right].$$9. The apparent camera-space velocities were superluminal or subluminal depending on geometry, but after correction the pulse propagates at $$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$0 in real time. The central-pixel intensity versus angle matched

$$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$1

with $$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$2 and typical point-wise error $$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$3.

In bright-field transmission microscopy, "Spectroscopic Approach to Correction and Visualisation of Bright-Field Light Transmission Microscopy Biological Data" (Platonova et al., 2019) formulates OIA as a spectrally informed calibration of the entire optical path. The channel-specific transfer function is defined as

$$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$4

or, when independently measured path transmission is included,

$$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$5

The photon-proportional radiant flux reaching a pixel of channel $$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$6 behind filter $$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$7 is

$$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$8

and per-pixel calibration curves $$\Delta E=\int_\Omega [I_{\mathrm{ref}}(x,y)-I_{\mathrm{corr}}(x,y)]\,dx\,dy,$$9 convert dark-corrected raw counts to corrected intensities

$$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$0

The corrected data are then compressed to 8 bpc using the Least Information Loss algorithm, whose objective is described as preserving every occupied gray level and thereby minimizing information loss.

The AI-driven OCT-SLO formulation is different again. In "Self-calibrating Intelligent OCT-SLO System" (Goswami, 2024), OIA is realized as sample specific automatic contrast adjustment of the beam on a pre-instructed region of interest. A variable-focus liquid lens and a stepper-motorized OCT reference arm are swept over settings; for each setting the system acquires 360 B-scans and constructs a 3D OCT volume and an en-face projection; an AI observer scores each volume and locks in the voltage and mirror-position settings with the highest score. The final implementation uses MobileNet-V2, which runs in $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$1 s per volume versus $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$2–$$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$3 s for U-Net. Relative to AI-driven automation, a purely SNR-maximization loop was reported to be $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$4 less accurate and $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$5 slower, while a complete two-knob sweep and convergence occurred in $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$6 s versus $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$7 min manually. The reported spatial resolution results were 2.41 $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$8m in a glass-bead phantom in the axial direction, 0.76 with STD 0.46 $$A^*=E_{\mathrm{ref}}/E_{\mathrm{corr}}\equiv \int_\Omega I_{\mathrm{ref}} \big/ \int_\Omega I_{\mathrm{corr}}.$$9m in mouse retina in the axial direction, and better than 228 line pair per millimeter or 2 $I_{\mathrm{err}}(x,y)$0m for all three spectrums in the $I_{\mathrm{err}}(x,y)$1–$I_{\mathrm{err}}(x,y)$2 plane.

4. Diffractive processors and learned optics-driven adjustment

Under spatially-incoherent illumination, OIA can be implemented as a universal linear map in intensity. "Universal Linear Intensity Transformations Using Spatially-Incoherent Diffractive Processors" (Rahman et al., 2023) shows that the time-averaged output intensity can be written as

$I_{\mathrm{err}}(x,y)$3

with

$I_{\mathrm{err}}(x,y)$4

so that the processor implements a linear map

$I_{\mathrm{err}}(x,y)$5

For arbitrary real, nonnegative intensity transforms, the reported degrees-of-freedom condition is

$I_{\mathrm{err}}(x,y)$6

where $I_{\mathrm{err}}(x,y)$7 is the total number of optimizable phase-only diffractive features. The work reports that MSE falls rapidly as $I_{\mathrm{err}}(x,y)$8 and saturates for $I_{\mathrm{err}}(x,y)$9; shallow networks with $\delta^*(x,y)$0 fail even at $\delta^*(x,y)$1; deeper stacks with $\delta^*(x,y)$2 fully unlock the available degrees of freedom; and indirect training yields lower MSE when $\delta^*(x,y)$3. The paper frames these results as the first demonstration of universal linear intensity transformations under spatially-incoherent illumination.

A learned, optics-driven variant appears in "MetaScope: Optics-Driven Neural Network for Ultra-Micro Metalens Endoscopy" (Li et al., 5 Aug 2025). There, OIA rectifies intensity decay by learning optical embeddings from two priors: a channel prior $\delta^*(x,y)$4 encoding per-wavelength focusing efficiency and a spatial prior $\delta^*(x,y)$5 encoding radial attenuation. The optical motivation is stated as wavelength-dependent meta-atom efficiency together with radial vignetting, producing an approximate sensor intensity

$\delta^*(x,y)$6

The learned embeddings are

$\delta^*(x,y)$7

which modulate channel and spatial attention:

$\delta^*(x,y)$8

$\delta^*(x,y)$9

The adjusted feature is formed by sequential channel and spatial gating with a residual skip:

$\phi(x,y)$0

OIA is inserted into the encoder blocks of a NAFNet-based encoder-decoder and is trained end-to-end under the restoration and segmentation objectives rather than a bespoke intensity-consistency term. Reported outcomes include a PSNR improvement of $\phi(x,y)$1 dB for OIA alone over the Meta-baseline, PSNR 34.85 dB for full OIA+OCC versus 34.13 dB without OIA, and an mDICE increase from 91.09% to 91.37% when adding OIA to the OCC-only model.

5. Longitudinal intensity shaping in high-intensity optics

In high-intensity laser systems, OIA is used to prescribe on-axis intensity and, in some formulations, on-axis group velocity over an extended focal region. "Axiparabola: a new tool for high-intensity optics" (Oubrerie et al., 2021) formulates the design problem as choosing an aspheric mirror so that

$\phi(x,y)$2

and, if required,

$\phi(x,y)$3

With radial coordinate $\phi(x,y)$4 and mirror sag $\phi(x,y)$5, the paraxial construction is based on the mapping from mirror radius to axial focus coordinate and on the relation

$\phi(x,y)$6

with on-axis irradiance

$\phi(x,y)$7

The paraxial sag equation is

$\phi(x,y)$8

Two worked examples are given. For a constant-intensity focal line, one obtains

$\phi(x,y)$9

with example parameters $Z_n(x,y)$0 mm, $Z_n(x,y)$1 mm, and $Z_n(x,y)$2 mm. For the constant-energy focal line relevant to plasma-waveguide generation, the empirical fit used is

$Z_n(x,y)$3

Upstream spatio-temporal couplings $Z_n(x,y)$4 are then used to tailor the effective on-axis group velocity.

"Programmable Focal Elongation and Shaping of High-Intensity Laser Pulses using Adaptive Optics" (Blum et al., 27 Sep 2025) realizes a related programmatic shaping by applying an adaptive phase mask

$Z_n(x,y)$5

with

$Z_n(x,y)$6

After an off-axis parabola of focal length $Z_n(x,y)$7, the total phase is

$Z_n(x,y)$8

and the on-axis intensity follows from the Fresnel integral. Representative simulated lineouts are reported as FWHM$Z_n(x,y)$9 mm without AO and FWHM$T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$00 mm with aberration $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$01m. Experimentally, with $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$02m, $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$03m, and $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$04m, the focal lengthening reached FWHM$T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$05 mm, corresponding to $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$06 for a native Rayleigh length $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$07 mm, with on-axis intensity variation below $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$08 over the central 30 mm and agreement with simulation within $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$09 error in FWHM$T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$10 and on-axis lineouts. The stated applications include HOFI waveguide generation and flying focus for dephasingless laser-wakefield acceleration.

6. Limitations, misconceptions, and outlook

Several limitations recur across OIA formulations. In I-AO, sensorless intensity modes are not strictly orthogonal, superposition can be imperfect, finite SLM phase-to-intensity calibrations limit dynamic range, and additional phase errors introduced by intensity correction must be removed by the DM (Zhao et al., 2024). In incoherent diffractive processors, depth matters: shallow networks fail even at large feature counts, which indicates that feature count alone does not guarantee realizability of a target intensity map (Rahman et al., 2023). In metalens endoscopy, OIA does not operate as a standalone correction term but is learned jointly with restoration and segmentation, so its effect depends on the surrounding architecture and supervision (Li et al., 5 Aug 2025).

A second misconception is that OIA can replace phase correction. The adaptive-optics literature states the opposite: in both sensor-based and sensorless I-AO, a DM phase routine follows intensity adjustment, and the reported gains are used to show that phase-only AO is insufficient when intensity errors dominate, not that phase is dispensable. Likewise, physics-based inversion methods such as light-in-flight imaging do not directly manipulate light; they recover true intensity by dividing out geometric-optics and scattering effects, so the adjustment is inferential rather than actuated (Morland et al., 2021).

The outlook in the literature is correspondingly heterogeneous. Proposed optimizations for I-AO include zonal or data-driven intensity correction bases, integration of Shack-Hartmann wavefront sensing in loop 1 for concurrent $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$11 and $T_{\mathrm{SLM}}(x,y)=\sin^2[\delta(x,y)/2].$12 measurement, and machine-learning accelerated mode selection and metric evaluation. Broader scenarios explicitly identified for OIA include astronomical telescopes with segmented mirrors, free-space optical links with atmospheric scintillation, laser machining through non-uniform media, visual processors operating under natural spatially-incoherent light, and biomedical imaging systems that must compensate structured intensity decay or sample-specific contrast variations (Zhao et al., 2024, Goswami, 2024). This suggests that OIA is best understood not as a single technique but as a general optics-informed design pattern for restoring, reshaping, or learning intensity in optical systems whose dominant errors are not purely phase aberrations.