Dynamic Adaptive Attention-Guided Self-Supervised Single Remote-Sensing Image Denoising

Optical remote-sensing images are widely used in many fields, and local complex texture details in images usually play a critical role in downstream tasks. However, noise interference will destroy the complex texture in the image, thus reducing the accuracy of downstream tasks. The current attention mechanism usually focuses on the global high-level features in the image, so it cannot effectively focus on the high-frequency information in the local complex texture in the remote-sensing image, and obtaining clean remote-sensing images to train neural networks is difficult. Therefore, applying the current depth learning-based natural image-denoising methods directly to optical remote-sensing images is challenging. To solve these problems, we propose a dynamic adaptive attention-guided self-supervised single remote-sensing image-denoising network (DAA-SSID). We construct a dynamic adaptive attention module (DAAM) by dynamically calculating the activation intensity of each neuron and combining the spatial feature information extracted from remote-sensing images. It can effectively extract complex texture features from remote-sensing images when only a single remote-sensing image participates in training. And we use independent random Bernoulli sampling in the training and inference stages, to prevent over-fitting caused by single-image training. Therefore, compared with other self-supervised denoising methods, our proposed model can denoise remote-sensing images with more complex textures when only a single image destroyed by noise is used as the training input. Experiments on synthetic additive Gaussian noise data and authentic noise data have shown that the proposed model achieves satisfactory results.