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

arXiv:2610.19218v1 Announce Type: new Abstract: 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 Z_2^0. The approach employs a U-Net architecture trained with a loss function combining reconstruction error minimization with spatial difference structural preservation through ell_1 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 Z_2^0, 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.

Related

• Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers
• Efficient All-in-One Weather Restoration using Spectral Harmonization
• Learning Projection-Aware 360-Degree Image Rectification via Dual-Projection Fusion

Source: arXiv cs.CV | 2026-10-15