Abstract: Currently,remote sensing images are widely used in environmental monitoring,disaster management and other fields.However,sensor failure or external environment results in the degradation of image quality in image acquisition influencing the application of remote sensing images.The DIL algorithm models different distortion degrees and distortion types,and uses the “backdoor” criterion in causal inference to derive the causal network for image restoration with fairly strong generalization ability.Therefore,this paper applies the DIL algorithm to remote sensing image restoration to use the collected remote sensing image data improving the restoration quality of remote sensing images.In this way,this paper uses DIL algorithm to improve the application of remote sensing images in environmental monitoring,disaster management and other fields.In this paper,the training data is normalized on the basis of the DIL algorithm to ensure that the variables of the training data are unique,so that they can better deal with the repair problems of remote sensing images.In the experiments,the DIL algorithm is used to denoise,rain and deblur the remote sensing images.The experimental results show that the DIL algorithm is better than the Noise2Noise,FFDNet,DnCNN and Restormer algorithms in remote sensing image restoration,and the image restoration quality is significantly improved.

Key words: Distortion invariant representation learning algorithm, Normalization, Causality, Remote sensing image restoration