Deep Learning for Remote Sensing Image SuperResolution The aim of image superResolution (SR) is to enhance image resolution while still retain the integrity of the original image. There are many ongoing types of research on image superresolution for natural images, but any a few on remote sensing images. In this paper, we proposed deep learningbased image superresolution techniques, including convolutional neural network (CNN) and generative adversarial network (GAN) to enhance the resolution of remote sensing images by a factor 4. In CNN, it learns an end to end mapping from lowresolution image to highresolution image whereas, in GAN, the model learns the mapping guided by the GAN loss and gives the sharper appearance in highresolution images. Our experimental results show that visually GAN models perform well but are inferior to other models in terms of image quality metrics, whereas quantitatively CNN models outperform other superresolution models.

SuperResolution via Wavelet Transform and Advanced Learning Techniques Image superresolution aims to generate a highresolution (HR) image from a lowresolution (LR) input image. In this paper, we propose a deep learningbased approach for image superresolution. We use the wavelet transform to separate the input image into four frequency bands, and train a model for each subband. By processing information from different frequency bands via different CNN models, we can extract features more efficiently and learn better LRtoHR mapping. In addition, we add dense connection to the model to make better use of the internal features in the CNN model. Furthermore, geometric selfensemble is applied in the testing stage to maximize the potential performance. Extensive experiments on four benchmark datasets show the efficiency of the proposed method.