A satellite image super-resolution method based on adversarial network and aerial image a priori

Abstract

The invention discloses a satellite image super-resolution method combining an adversarial network with an aerial image a priori. Firstly, an image pair composed of a 16-level noisy image and a corresponding 16-level non-noisy image is used for training a denoising model, and then the image super-resolution model is trained by using clear aerial data. Because there is no satellite image and aerialimage pairs, clear aerial images are used to construct an external priori dictionary of GMM model in the post-processing of the generated super-resolution images, and then the internal unclear satellite images are guided to be reconstructed. In order to further improve the image quality, Gaussian filter is used to sharpen the image after reconstruction. Finally, the high-resolution image of the original satellite image is obtained, and the visual quality of the image is improved based on the original satellite image. The effectiveness of the scheme can also be seen from the experiment. It provides an effective way to solve the problem of satellite image super-resolution and image quality improvement under the condition of conditional constraints in reality.

Description

technical field

[0001] The invention belongs to the technical field of image super-resolution, in particular to a satellite image super-resolution method based on multi-scale perceptual loss and generation confrontation network combined with aerial image prior. Background technique

[0002] Image resolution is an important indicator of image quality. An image with higher resolution can show more details more clearly. However, due to the influence of hardware and external environment during the image acquisition process, the acquired image resolution is lower, resulting in The problem of how to obtain high-resolution images from low-resolution images. At present, with the increase in the number of satellites, satellites can cover more than 90% of the earth, which makes the range that can be monitored by satellites much larger than the range covered by images obtained by other means, but satellite images are affected by many reasons. The rate is lower. For example, compared ...

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