Remote Sensing Image Denoising Network Based on Locally Enhanced Window Transformer

Remote sensing images serve as indispensable and pivotal carriers in diverse fields, including natural resource management, ecological environment protection, and urban–rural construction planning. Acquiring high-quality remote sensing images is particularly critical for practical applications. However, limitations such as adverse weather conditions and equipment constraints often hinder the acquisition of high-quality data. To tackle this challenge, this paper proposes a U-shaped Transformer network architecture—incorporating locally enhanced windows and denoted as ULTNet—for restoring low-quality remote sensing images to high-quality ones. The network leverages a locally enhanced transformer (LT) block, equipped with locally enhanced windows, for attention extraction. This block applies a local self-attention mechanism within nonoverlapping spatial windows, effectively reducing computational complexity while capturing richer global dependencies to enable end-to-end remote sensing image restoration. Both quantitative and qualitative analyses demonstrate that the proposed method exhibits superior restoration performance and strong generalization capabilities.