DATA AUGMENTATION APPROACHES FOR SATELLITE IMAGE SUPER-RESOLUTION

Super-resolution for unregistered satellite images

A Deep Model for Super-resolution Enhancement from a Single Image

This study presents a method to reconstruct a high-resolution image using a deep convolution neural network. We propose a deep model, entitled Deep Block Super Resolution (DBSR), by fusing the output features of a deep convolutional network and a shallow convolutional network. In this way, our model benefits from high frequency and low frequency features extracted from deep and shallow networks...

Image Super - Resolution Members :

Super-resolution is the task of obtaining a high-resolution image of a scene given low resolution image(s) of the scene. Applications of super-resolution include forensic, satellite, medical imaging, surveillance, displaying video on large screens, etc [1]. Obtaining high-resolution images directly via better hardware (better image sensors, larger chip size) is quite costly. Furthermore, most o...

Single-image High-resolution Satellite Data for 3d Information Extraction

The demand for accurate and up-to-date spatial information is increasing and its availability is becoming more important for a variety of tasks. Today’s commercial high-resolution satellite imagery (HRSI) offers the potential to extract useful and accurate spatial information for a wide variety of mapping and GIS applications. The extraction of metric information from images is possible due to ...

Single Image Super-Resolution

Image super-resolution is the task of obtaining a high-resolution (HR) image of a scene given low-resolution (LR) image(s) of the scene. In this project, we have focused on the task of super-resolution given a single LR image, which is usually the case. There exist many techniques in literature addressing this task, and we have considered two techniques having the essence of [1] and [2]. In fir...