An Enhanced MIMO-Unet Model for CHASE High Resolution H Α Image Deblurring

The Chinese H α Solar Explorer (CHASE) satellite provided invaluable full-disk H α spectral data, but images from its initial mission phase (before August 2022) suffered from significant defocus blur, limiting their scientific utility. This paper proposes an enhanced Multi-Input Multi-Output U-Net (MIMO-Unet) model to address this blind deconvolution challenge. Because sharp ground-truth counterparts are unavailable for real early-phase CHASE observations, we constructed a physically constrained dataset by applying a Zernike-polynomial-based defocus model to sharp CHASE/HIS observations acquired after the focusing condition became stable. The proposed model enhances the baseline MIMO-Unet by integrating Squeeze-and-Excitation (SE) blocks into the encoder for adaptive channel-wise feature recalibration and an Atrous Spatial Pyramid Pooling (ASPP) module into the feature fusion stage to capture multi-scale context. On the Zernike-simulated CHASE defocus dataset, the proposed method improves PSNR from 27.0169 dB to 33.8707 dB, corresponding to a 6.85 dB gain, and increases SSIM, FSIM, and the multi-fractal texture-based Perception Evaluation (PE) metric by 45.67 α observations, with quantitative validation on Zernike-simulated defocus data and preliminary qualitative tests on real early-phase CHASE observations.