HIERARCHICAL DENOISING MODEL BASED ON DEEP LOW-RANK REPRESENTATION

Noise reduction is a critical research area in current remote sensing image processing. Existing denoising techniques for remote sensing images often encounter challenges such as blurred edges and excessive smoothing. To overcome these limitations, we propose a novel hierarchical denoising model based on an Autoencoder. Our model effectively addresses the issue of distinguishing low-rank residuals and preserving essential details in remote sensing images, while also extracting edge features from the residuals with high efficiency. To validate the effectiveness of our approach, we conduct comprehensive experimental tests on a representative remote sensing dataset. The results demonstrate that our method successfully preserves edge details while achieving superior denoising performance compared to state-of-the-art techniques.