Remote sensing image denoising based on deformable convolution and attention-guided filtering in progressive framework

Remote sensing image denoising tasks are challenged by complex noise distributions and multiple noise types, including a mixture of additive Gaussian white noise (AWGN) and impulse noise (IN). For better image recovery, complex contextual information needs to be balanced while maintaining spatial details. In this paper, a denoising model based on multilevel progressive image recovery is proposed to address the problem of remote sensing image denoising. In our model, the deformable convolution improves spatial feature sampling to effectively capture image details. Meanwhile, attention-guided filtering is used to pass the output images from the first and second stages to the third stage in order to prevent information loss and optimize the image recovery effect. The experimental results show that under the mixed noise scene of Gaussian and pepper noise, our proposed model shows superior performance relative to several existing methods in terms of both visual effect and objective evaluation indexes. Our model can effectively reduce the influence of image noise and recover more realistic image details.