SARFT-GAN: Semantic-Aware ARConv Fused Top-k Generative Adversarial Network for Remote Sensing Image Denoising

Optical remote sensing images play a pivotal role in numerous applications, notably feature recognition and scene semantic segmentation. Nevertheless, their efficacy is frequently compromised by various noise types, which detrimentally impact practical usage. We have meticulously crafted a novel attention module amalgamating Adaptive Rectangular Convolution (ARConv) with Top-k Sparse Attention. This design dynamically modifies feature receptive fields, effectively mitigating superfluous interference and enhancing multi-scale feature extraction. Concurrently, we introduce a Semantic-Aware Discriminator, leveraging visual-language prior knowledge derived from the Contrastive Language–Image Pretraining (CLIP) model, steering the generator towards a more realistic texture reconstruction. This research introduces an innovative image denoising model termed the Semantic-Aware ARConv Fused Top-k Generative Adversarial Network (SARFT-GAN). Addressing shortcomings in traditional convolution operations, attention mechanisms, and discriminator design, our approach facilitates a synergistic optimization between noise suppression and feature preservation. Extensive experiments on RRSSRD, SECOND, a private Jilin-1 set, and real-world NWPU-RESISC45 images demonstrate consistent gains. Across three noise levels and four scenarios, SARFT-GAN attains state-of-the-art perceptual quality—achieving the best FID in all 12 settings and strong LPIPS—while remaining competitive on PSNR/SSIM.