Weighted Sparse Image Quality Restoration Algorithm for Small-Pixel High-Resolution Remote Sensing Data

The demand for high-spatial-resolution optical remote sensing applications is increasing, while conventional high-resolution optical payloads face limitations in widespread application due to their large size and high manufacturing costs. With the rapid development of image processing technology, we adopt a method combining small-pixel detector sampling with image deblurring algorithms to obtain high-spatial-resolution remote sensing images. In this work, we use Zernike polynomials to simulate diffraction-blurred small-pixel images under various aberration modulations, ensuring the simulation data follow solid physical principles. Furthermore, we propose a new weighted sparse model ℓwe that combines the Welsch-weighted ℓ1-norm with ℓ0-norm constraints, and further applies ℓwe regularization to both gradient fidelity terms and image gradient terms to enhance fidelity constraints and improve latent structure preservation. Compared with other sparse models, our model produces results with fewer residual structures and stronger sparsity. Comprehensive evaluations on both simulated small-pixel remote sensing datasets and real-world remote sensing images demonstrate that the proposed weighted sparse image quality restoration algorithm achieves more desirable results with excellent robustness. Compared to other methods, the proposed approach improves PSNR by an average of 2.5% and SSIM by 2.2%, while reducing ER by 20.7%. This provides an effective technical solution for image quality restoration of small-pixel remote sensing data.