# A Gradient-Regularized Deep Learning Method for Restoring Image in Myopic Vision

arXiv: https://arxiv.org/abs/2610.19218 · HTML: https://mlsanity.com/paper/2610.19218/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 13 Oct 2026.

## Builds on (most cited first, 4 of 4)

- [Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal](https://mlsanity.com/paper/1808.10848/) (2018)
- [High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving](https://mlsanity.com/paper/2504.11472/) (2025)
- [Autoregressive High-Order Finite Difference Modulo Imaging: High-Dynamic Range for Computer Vision Applications](https://mlsanity.com/paper/2504.04228/) (2025)
- [Projection-Based Correction for Enhancing Deep Inverse Networks](https://mlsanity.com/paper/2505.15777/) (2025)

## Abstract (arXiv)

Myopic vision degrades the quality of human vision due to spherical eye defocus aberrations that impair the perception of fine spatial details. To address this limitation, this work proposes a deep learning-based approach to computationally design transformed images that allow individuals with myopia in fixed distance to perceive scenes with enhanced clarity. The method trains a neural network to counteract defocus aberrations modeled using the second-order Zernike polynomial Z20. The approach employs a U-Net architecture trained with a loss function combining reconstruction error minimization with spatial difference structural preservation through ℓ1 regularization. Training is carried out exclusively on the KITTI dataset, while testing is performed on DIV2K and ImageNet datasets, demonstrating generalization across diverse image distributions. Under myopic conditions modeled by Z20, baseline performance shows visual degradation with PSNR values ranging from 19.47 to 27.42 dB across datasets and severity levels. The proposed gradient-regularized method achieves consistent improvements, delivering PSNR gains ranging from 1.48 to 7.34 dB and significantly lower perceptual compared to classical baselines.