# Zernike-Parameterized Forward Regularizer

GridPSF adds an inspectable forward constraint to the MambaTM restoration network's training graph: from the observed clip and the host's latent field it predicts an 8×8 grid of 35 piston-free Zernike coefficients per cell, then blends unit-mass point-spread functions rendered by a Fraunhofer model to fit the turbulent observation. On held-out ATSyn-dynamic videos, forward consistency reaches 29.49 dB PSNR and 0.8882 SSIM, 3.95 dB above the 25.54 dB identity floor of using the clean target as the prediction. One validation-selected matched-host checkpoint pair gives +0.419 dB PSNR and +0.0022 SSIM, but LPIPS rises by 0.0017, and a five-seed control detects no average restoration gain.

GridPSF adds an inspectable forward constraint to the MambaTM restoration network's training graph: from the observed clip and the host's latent field it predicts an 8×8 grid of 35 piston-free Zernike coefficients per cell, then blends unit-mass point-spread functions rendered by a Fraunhofer model to fit the turbulent observation. On held-out ATSyn-dynamic videos, forward consistency reaches 29.49 dB PSNR and 0.8882 SSIM, 3.95 dB above the 25.54 dB identity floor of using the clean target as the prediction. One validation-selected matched-host checkpoint pair gives +0.419 dB PSNR and +0.0022 SSIM, but LPIPS rises by 0.0017, and a five-seed control detects no average restoration gain.