Fusion of Deep and Non-Deep Methods for Fast Super-Resolution of Satellite Images

TL;DR

This paper introduces a hybrid super-resolution framework for satellite images that selectively applies deep or non-deep methods based on regional content, significantly reducing processing time while maintaining high image quality.

Contribution

It presents a novel region-aware super-resolution approach that combines deep and non-deep methods to optimize speed and quality for large satellite images.

Findings

Substantial reduction in inference time.

Achieved comparable image quality to deep SR methods.

Effective regional content analysis improves efficiency.

Abstract

In the emerging commercial space industry there is a drastic increase in access to low cost satellite imagery. The price for satellite images depends on the sensor quality and revisit rate. This work proposes to bridge the gap between image quality and the price by improving the image quality via super-resolution (SR). Recently, a number of deep SR techniques have been proposed to enhance satellite images. However, none of these methods utilize the region-level context information, giving equal importance to each region in the image. This, along with the fact that most state-of-the-art SR methods are complex and cumbersome deep models, the time taken to process very large satellite images can be impractically high. We, propose to handle this challenge by designing an SR framework that analyzes the regional information content on each patch of the low-resolution image and judiciously chooses to use more computationally complex deep models to super-resolve more structure-rich regions on the image, while using less resource-intensive non-deep methods on non-salient regions. Through extensive experiments on a large satellite image, we show substantial decrease in inference time while achieving similar performance to that of existing deep SR methods over several evaluation measures like PSNR, MSE and SSIM.