Image Restoration for Remote Sensing: Overview and Toolbox

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS

MULTISPECTRAL IMAGE RESTORATION USING A VECTOR-VALUED REACTION-DIFFUSION BASED MIXED NOISE REMOVAL TECHNIQUE

A novel multispectral image filtering technique is proposed in this article. Since the multispectral images are often corrupted by mixed Poisson-Gaussian noise during the sensing and acquisition process, a nonlinear anisotropic diffusion-based restoration approach that deals efficiently with this type of noise mixture is considered here. A second-order vector-valued reaction-diffusion model that leads to a system of well-posed single-valued anisotropic diffusion equations coupled by correlation terms is introduced for this purpose. A finite difference method-based fast-converging approximation algorithm that solves numerically this nonlinear diffusion-based system is then proposed. This iterative numerical approximation scheme is successfully used for removing both the additive Gaussian and quantum noises while preserving the essential features of the multi-valued image. The effectiveness of the described mixed denoising technique is illustrated by the results of the restoration experiments and method comparisons that are also presented here. The proposed restoration approach enhances considerably the spectral image quality, making it well-prepared for the further MSI analysis and computer vision processes, such as the geospatial and remote sensing image analysis

Mapping the Physical Properties of Cosmic Hot Gas with Hyper-spectral Imaging

A novel inversion technique is proposed to compute parametric maps showing the temperature, density and chemical composition of cosmic hot gas from X-ray hyper-spectral images. The parameters are recovered by constructing a unique non-linear mapping derived by combining a physics-based modelling of the X-ray spectrum with the selection of optimal bandpass filters. Preliminary results and analysis are presented.Comment: 6 pages, 5 figures; accepted by the 5th IEEE Workshop on Application of Computer Vision (WACV/MOTION 2005), Breckenridge, CO, USA, 2005; uses ieee.cls (included). For a pdf version with full-resolution figures, try http://www.cs.bham.ac.uk/~exc/Research/Papers/ieee_astro_05.pd