Traditional image processing algorithms lack stability， and deep learning algorithms fall short of engineering requirements for remote sensing due to insufficient training datasets and high computational demands. To tackle this， the paper integrates degradation modeling and image processing. It introduces a method for creating a remote sensing image enhancement dataset using a Zernike polynomial degradation model. Additionally， it designs an algorithm for enhancing degraded remote sensing images using a hybrid frequency-domain and spatial-domain attention mechanism. This algorithm employs a dual-domain selection module and a frequency feature residual module to improve the learning of high-frequency image textures and details in both domains. The hybrid attention mechanism further boosts feature extraction capabilities. The algorithm's performance was validated against five common methods using NIQE values， visualization effects， MTF curves， and inference efficiency. Results show that the proposed approach significantly reduces NIQE values and improves MTF curves， leading to clearer images and substantially enhancing degraded image quality. For images with a specific pixel size of 27 620×29 200， the algorithm processes them in just 27 s， compared to hours for traditional methods， thus meeting engineering timeliness requirements. This research offers a rapid and effective solution for addressing satellite imaging degradation.