Self-Supervised Denoising via Blind Feature Extraction and Diffusion-Based Texture Generation

In the field of remote sensing, detection in dimly lit or shadowed areas has traditionally been difficult because of detector noise. Given that noise in real-world images of remote sensing exhibits spatial correlation, existing self-supervised methods encounter difficulties in reconciling the suppression of spatially correlated noise with the preservation of local texture details. To address this challenge, we propose a self-supervised model that combines blind-spot feature extraction with diffusion-based texture generation to fine denoising of real-world images under adverse conditions. We first introduce a blind-spot feature extraction structure based on the fusion of U-Net with blind-spot net (UBSN) and blind transformer (BTF). In UBSN, we integrate multistride blind-spot convolution (BSC) + dilated convolution (DC) feature extraction nodes and employ a Reshuffle strategy in skip-layer connections to maintain large-scale blind-spot characteristics. Additionally, we design a transformer structure for blind spot between patches to remove the noise with spatial correlations while ensuring global feature acquisition. Subsequently, to restore texture details blurred by the blind-spot structures, we introduce a texture generation diffusion structure during model training, achieving a balance between large-scale blind-spot characteristics and local rich texture details. Experimental results demonstrate that our approach outperforms other self-supervised denoising methods, even some methods leveraging unpaired images, without the need for parameters related to on-orbit satellite detectors.