Remote sensing image deblurring algorithm based on WGAN

Abstract

Remote sensing images are blurred due to large and wide imaging, long shooting distance, fast scanning speed, interference from external light, etc. At the same time, because of that remote sensing images have the characteristics of diverse and dense shooting objects, deblurring remote sensing images is a major problem in remote sensing research. Therefore, we propose a remote sensing image deblurring algorithm, which based on WGAN. The algorithm is different from the traditional method in estimating the blur kernel of image. What’s more our method does not require an explicit estimation of the blur kernel, and it implements an end-to-end image deblurring process. We use a WGAN-based deblurring model. First, the training images are processed in pairs. Then, in order to increase the generalization ability, a image of 256 * 256 that is a sub-region cropped at the random position in the original image is chosen as the input image. Finally, to achieve a better deblurring effect, a content loss function and a perceptual loss function are added to the loss function to achieve the specific implementation. The remote sensing image deblurring model trained by the proposed method has achieved better results on the remote sensing image dataset. The experimental results show that the proposed algorithm have better performance than the traditional method in filtering out the blur of remote sensing images, which could optimize the overall visual effect subjectively and improve the peak signal-to-noise ratio of the image objectively.