This paper reviews the second challenge on spectral reconstruction from RGB images, ie,
the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image …

Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has
gained a lot of interest in the remote sensing community, who sees it as an opportunity to …

Spectral imaging sensors often suffer from low spatial resolution, as there exists an essential
tradeoff between the spectral and spatial resolutions that can be simultaneously achieved …

In this paper we analyze the most used measures for the assessment of spectral similarity of
reflectance and radiance signals. First of all we divide them in five groups on the basis of the …

Hyperspectral images are a valuable tool for remotely sensing important characteristics of a
variety of landscapes, including water quality and the status of marine disasters. However …

In this paper, we describe the Perceptual Image Restoration and Manipulation (PIRM)
workshop challenge on spectral image superresolution, motivate its structure and conclude …

Self‐attention has been successfully leveraged for long‐range feature‐wise similarities in
deep learning super‐resolution (SR) methods. However, most of the SR methods only …

Most super-resolution methods are trained on datasets where high-resolution images and
corresponding low-resolution images are obtained by the fixed degradation method …

Existing single image super-resolution methods are either designed for synthetic data, or for
real data but in the RGB-to-RGB or the RAW-to-RGB domain. This paper proposes to zoom …

Spectral information provided by multispectral and hyperspectral sensors has a great impact
on remote sensing studies, easing the identification of carbonate outcrops that contribute to …