# Convolutional neural networks for fast myopic deconvolution of AO observations

Pierre Vermot

► To cite this version:

Pierre Vermot. Convolutional neural networks for fast myopic deconvolution of AO observations. 2025, 10.5281/zenodo.17775738. hal-05520431

HAL Id: hal-05520431

https://hal.science/hal-05520431v1

Submitted on 20 Feb 2026

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
CONVOLUTIONAL NEURAL NETWORKS
FOR FAST MYOPIC DECONVOLUTION
OF AO OBSERVATIONS

Pierre Vermot (LIRA, Paris Observatory, PSL)
AO4ELT8 – 28/10/2025

R. Petrov (ANR AGN_MELBA), D. Gratadour (PEPR Origins), Y. Clénet, A. Berdeu, B. Besnard, ...

<figure><img src="image_2.png" /></figure>
# TALK PLAN - WORKFLOW

I. Sky model - Targets image generation

II. Instrumental model – Observations generation

III. CNN architecture and training

IV. Testing

V. Applications

VI. Conclusions
# I. SKY MODEL

## Image generation algorithm

* Combination of Fourier Random Fields:

* Powerlaw spectrum + random phase

* Fourier Transform

* Point sources

<figure><img src="image_3.png" /></figure>
## II. INSTRUMENTAL MODEL – OBSERVATIONS GENERATION

### 1. Short exposure PSFs

* Residual atmospheric phase:

* Combination of Zernike modes

* Random Strehl ratio

* Pupil

* FFT

<figure><img src="image_4.png" /></figure>
## II. INSTRUMENTAL MODEL – OBSERVATIONS GENERATION

### 2. Long exposure PSFs

Combination of N short exposure PSFs

### 3. CCD frame generation

* Convolution of Source image with LE PSF

* Photon noise, read-out noise

### 4. Full observation

* Repeat steps (1, 2, 3) to generate data cubes with K images of the same object

<figure><img src="image_5.png" /></figure>
# CNN ARCHITECTURE AND TRAINING

## U-NET model

- Fully convolutional auto-encoder

- Skip connections

- Widespread architecture for image segmentation, deconvolution/super-resolution, computer vision

<figure><img src="image_6.png" /></figure>
# CNN ARCHITECTURE AND TRAINING

## U-NET training

* Trained to reconstruct the original image (output) based on the convolved datacube (input)

* Minimizes Mean Squared Error

* 1M examples, up to 50 epochs

* Training ~ several hours

* Inference ~ $10^{-3}$ s

<figure><img src="image_7.png" /></figure>
Ground Truth

<figure><img src="image_8.png" /></figure>

Observation

<figure><img src="image_9.png" /></figure>

NEBRAA

<figure><img src="image_10.png" /></figure>

Richardson-Lucy

<figure><img src="image_11.png" /></figure>

GT - Observation

<figure><img src="image_12.png" /></figure>

GT - NEBRAA

<figure><img src="image_13.png" /></figure>

GT - Richardson-Lucy

<figure><img src="image_14.png" /></figure>
# COMPARISON WITH CLASSICAL DECONVOLUTION ALGORITHMS

## Evaluation on realistic data

* Real IR astronomical images: rebinned 2MASS K-band

* Experimental PSF: NaCo bright star

* Several metrics:

* Squared residuals

* R2

* Universal Image Quality Index

* Several algorithms:

* Richardson-Lucy

* MAP

* Wiener

<figure><img src="image_15.png" /></figure>
APPLICATIONS: NGC 1068 IN THE M BAND WITH NACO

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /><figcaption>Gratadour+2006
(MISTRAL)</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>Charles+2025</figcaption></figure>
# APPLICATIONS: GALACTIC CENTER IN THE M BAND

## NO NEED TO TRAIN AGAIN

<figure><img src="image_19.png" /></figure>
# NGC 1068 WITH THE LBTI (ISBELL+2024)

## 10000 FRAMES DECONVOLVED IN A FEW SECONDS
DIFFERENTIAL PISTON

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>
(BONUS) INTERFEROMETRY:
SANCHEZ+2022 SPIE IMAGING CONTEST (APERTURE MASKING)

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>
# CONCLUSION

* Workflow:
1. Simulated datasets

2. U-NET training

3. Application to actual observations

* Outperforms simple algorithms (RL, MAP, Wiener)

* Well suited for automated deconvolution:
1. Very fast (<< 1 ms)

2. No need for a reference PSF

3. Robust to high levels of noise
# I. SKY MODEL

## Algorithm 1

* Random combination of elementary shapes:

* Disks, rings, gaussians, etc

* Point sources

## Algorithm 2

* Combination of Fourier Random Fields:

* Powerlaw spectrum + random phase

* Fourier Transform

<figure><img src="image_26.png" /></figure>
# COMPARISON WITH CLASSICAL DECONVOLUTION ALGORITHMS

## Evaluation on the validation dataset

* Several metrics:

* Squared residuals

* R2

* Universal Image Quality Index

* Several algorithms:

* Richardson-Lucy

* MAP

* Wiener

<figure><img src="image_27.png" /></figure>