Restoration of vibration-induced remote sensing images based on CNN identification: a comparative approach

Abstract

ABSTRACT Satellite vibrations during the imaging process frequently cause images to blur. Satellite micro-vibration types vary according to their sources and each type causes a certain form of image blur. In this paper, the effectiveness of using Convolution neural networks (CNNs) in identifying the types of micro-vibration that lead to image blur is addressed. Additionally, a comparative study between three restoration techniques for the restoration of vibration-induced remote sensing images is considered. The utilised restoration technique is based on a two-steps approach: the first step is the identification of the type of vibration using CNNs to identify the kernel shape or the point spread function that causes image degradation. Hence, the kernel function that blurs the image is accurately determined. The second step is the restoration of the degraded image using three different techniques including Modified Levenberg – Marquardt (MLM), Approximate Land-weber (AL), and Total Variation (TV). The restored images were evaluated using four image quality metrics. MLM technique is designed to improve the convergence and accuracy of the deblurring process. The key finding of this paper features the effectiveness of the MLM technique in image restoration compared to AL and TV techniques.