Remote sensing image defogging method based on edge sharpening cyclic generative adversarial network

Abstract

The invention provides a remote sensing image defogging method based on an edge sharpening cyclic generative adversarial network. According to the method, the foggy remote sensing image is processed by utilizing the deep generative adversarial network, and the ground object information of the foggy remote sensing image can be automatically recovered in a large quantity; secondly, the invention provides a cyclic generative adversarial network model added with an image sharpening mechanism, and the model improves the condition that textures of pictures generated by the generative adversarial network are not clear, and improves the discrimination capability of blurred images; and finally, the VGG16 network pre-training process of calculating the perception consistency loss function by using the model is improved, and the VGG16 network is pre-trained by using the remote sensing image, so that the characteristics of the remote sensing image are better extracted, and the calculation of the perception loss error is more accurate. Compared with an existing remote sensing image defogging method, the remote sensing image defogging method has the advantages of being intelligent, batched and automatic, the quality of the remote sensing image can be remarkably improved, detail information such as remote sensing image textures is protected, and a good recovery effect is achieved.

Description

technical field [0001] The invention relates to the field of image processing, and more specifically, to a method for defogging remote sensing images based on deep learning. Background technique [0002] With the development of national aerospace technology, remote sensing images are used in all aspects of society. Due to natural, man-made and other reasons, fog has become a very common noise factor in remote sensing images. Fog will reduce the visibility of the atmosphere, blur the images obtained by optical equipment, especially based on the remote sensing platform, and make it impossible to obtain clear ground object information from the obtained remote sensing images. This will bring great difficulties to related applications based on remote sensing images, such as monitoring, automatic navigation, and target extraction. Researching an effective remote sensing image defogging method can make remote sensing images serve the current application more effectively. [0003...

AI Technical Summary

Problems solved by technology

Among them, the non-model-based defogging method is also called image enhancement method; this method does not need to consider the cause and model of image degradation when processing, and only needs to use conventional image enhancement methods to improve the image quality according to specific requirements. Visual effects are not based on the essence of optical imaging to achieve image defog processing; but the cloud thinning algorithm based on image enhancement needs to perform multi-scale transformation and inverse transformation, so the calculation is large and relatively complicated

The model-based defogging method, also known as the image restoration method, uses the principle of image degradation to achieve image defogging from the essence of optical imaging; however, due to the physical model it uses, it often requires the known scene depth and atmospheric conditions and other prior information, but for remote sensing images, it needs to obtain more information, so the use of image restoration methods brings a certain degree of difficulty to the restoration of remote sensing images

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