APPLICATION OF PRE-TRAINED REAL-WORLD SUPER RESOLUTION MODELS TO OPTICAL SATELLITE IMAGE

Abstract

The single image super-resolution (SISR) technique refers to improving the resolution over the original image. In recent years, we use deep learning-based convolutional neural networks to improve the spatial resolution of images more reasonably. To train such deep learning models, we use training samples consisting of the original HR images and LR images obtained by bicubic downsampling. However, this method of training data using downsampling has a negative impact when applying the trained model to real images. That is because the downsampling function that occurs in the real image is unknown, and the hypothetically created LR image does not represent the resolution degradation that can realistically occur. Therefore, SISR methods that use realistic degradation called real-world super-resolution (RWSR) have been proposed. In this paper, we investigate how such RWSR methods using realistic degradation affect the SISR performance of satellite images. The results of applying the trained model to optical satellite images show that the RWSR method is not the most effective way to handle optical satellite images when compared to the deep learning method without modeling the degradation. In particular, we showed that the effect of RWSR with not only upsampling but also noise and blur removal is significant in the visibility of optical satellite images. Moreover, pre-trained RWSR models can be an aid in visually deciphering ground objects.