Superresolution Enhancement of Hyperspectral CHRIS/Proba Images With a Thin-Plate Spline Nonrigid Transform Model

Given the hyperspectral-oriented waveband configuration of multiangular CHRIS/Proba imagery, the scope of its application could widen if the present 18-m resolution would be improved. The multiangular images of CHRIS could be used as input for superresolution (SR) image reconstruction. A critical procedure in SR is an accurate registration of the low-resolution images. Conventional methods based on affine transformation may not be effective given the local geometric distortion in high off-nadir angular images. This paper examines the use of a non-rigid transform to improve the result of a nonuniform interpolation and deconvolution SR method. A scale-invariant feature transform is used to collect control points (CPs). To ensure the quality of CPs, a rigorous screening procedure is designed: 1) an ambiguity test; 2) the m-estimator sample consensus method; and 3) an iterative method using statistical characteristics of the distribution of random errors. A thin-plate spline (TPS) nonrigid transform is then used for the registration. The proposed registration method is examined with a Delaunay triangulation-based nonuniform interpolation and reconstruction SR method. Our results show that the TPS nonrigid transform allows accurate registration of angular images. SR results obtained from simulated LR images are evaluated using three quantitative measures, namely, relative mean-square error, structural similarity, and edge stability. Compared to the SR methods that use an affine transform, our proposed method performs better with all three evaluation measures. With a higher level of spatial detail, SR-enhanced CHRIS images might be more effective than the original data in various applications.JRC.H.7-Climate Risk Managemen

HYPERSPECTRAL LINE-SCANNING MICROSCOPY FOR HIGH-SPEED MULTICOLOR QUANTUM DOT TRACKING

One of the challenges in studying protein interactions in live cells lies in the capacity to obtain both spatial and temporal information that is sufficient to extend existing knowledge of the dynamics and interactions, especially when tracking proteins at high density. Here we introduce a high-speed laser line-scanning hyperspectral microscope that is designed to track quantum dot labeled proteins at 27 frames/sec over an area of 28 um2 using 128 spectral channels spanning the range from 500 to 750 nm. This instrument simultaneously excites 8 species of quantum dots and employs a custom prism spectrometer and high speed EMCCD to obtain spectral information that is then used to distinguish and track individual probes at high density. These emitters are localized to within 10s of nm in each frame and reconstructed trajectories yield information of the protein dynamics and interactions. This manuscript describes the design, implementation, characterization, and application of a high-speed laser line-scanning hyperspectral microscope (HSM). The intended primary application is that of investigating the dynamics of transmembrane antibody receptors using quantum dot labeled immunoglobulin E (QD-IgE). Several additional examples demonstrate other advantages and applications of this method, including 3D hyperspectral imaging of live cells and hyperspectral superresolution imaging

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Spectral Super-Resolution of Satellite Imagery with Generative Adversarial Networks

Hyperspectral (HS) data is the most accurate interpretation of surface as it provides fine spectral information with hundreds of narrow contiguous bands as compared to multispectral (MS) data whose bands cover bigger wavelength portions of the electromagnetic spectrum. This difference is noticeable in applications such as agriculture, geosciences, astronomy, etc. However, HS sensors lack on earth observing spacecraft due to its high cost. In this study, we propose a novel loss function for generative adversarial networks as a spectral-oriented and general-purpose solution to spectral super-resolution of satellite imagery. The proposed architecture learns mapping from MS to HS data, generating nearly 20x more bands than the given input. We show that we outperform the state-of-the-art methods by visual interpretation and statistical metrics.Les dades hiperspectrals (HS) són la interpretació més precisa de la superfície, ja que proporciona informació espectral fina amb centenars de bandes contigües estretes en comparació amb les dades multiespectrals (MS) les bandes cobreixen parts de longitud d'ona més grans de l'espectre electromagnètic. Aquesta diferència és notable en àmbits com l'agricultura, les geociències, l'astronomia, etc. No obstant això, els sensors HS manquen als satèl·lits d'observació terrestre a causa del seu elevat cost. En aquest estudi proposem una nova funció de cost per a Generative Adversarial Networks com a solució orientada a l'espectre i de propòsit general per la superresolució espectral d'imatges de satèl·lit. L'arquitectura proposada aprèn el mapatge de dades MS a HS, generant gairebé 20x més bandes que l'entrada donada. Mostrem que superem els mètodes state-of-the-art mitjançant la interpretació visual i les mètriques estadístiques.Los datos hiperspectral (HS) son la interpretación más precisa de la superficie, ya que proporciona información espectral fina con cientos de bandas contiguas estrechas en comparación con los datos multiespectrales (MS) cuyas bandas cubren partes de longitud de onda más grandes del espectro electromagnético. Esta diferencia es notable en ámbitos como la agricultura, las geociencias, la astronomía, etc. Sin embargo, los sensores HS escasean en los satélites de observación terrestre debido a su elevado coste. En este estudio proponemos una nueva función de coste para Generative Adversarial Networks como solución orientada al espectro y de propósito general para la super-resolución espectral de imágenes de satélite. La arquitectura propuesta aprende el mapeo de datos MS a HS, generando casi 20x más bandas que la entrada dada. Mostramos que superamos los métodos state-of-the-art mediante la interpretación visual y las métricas estadísticas

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution

Recently, convolutional networks have achieved remarkable development in remote sensing image Super-Resoltuion (SR) by minimizing the regression objectives, e.g., MSE loss. However, despite achieving impressive performance, these methods often suffer from poor visual quality with over-smooth issues. Generative adversarial networks have the potential to infer intricate details, but they are easy to collapse, resulting in undesirable artifacts. To mitigate these issues, in this paper, we first introduce Diffusion Probabilistic Model (DPM) for efficient remote sensing image SR, dubbed EDiffSR. EDiffSR is easy to train and maintains the merits of DPM in generating perceptual-pleasant images. Specifically, different from previous works using heavy UNet for noise prediction, we develop an Efficient Activation Network (EANet) to achieve favorable noise prediction performance by simplified channel attention and simple gate operation, which dramatically reduces the computational budget. Moreover, to introduce more valuable prior knowledge into the proposed EDiffSR, a practical Conditional Prior Enhancement Module (CPEM) is developed to help extract an enriched condition. Unlike most DPM-based SR models that directly generate conditions by amplifying LR images, the proposed CPEM helps to retain more informative cues for accurate SR. Extensive experiments on four remote sensing datasets demonstrate that EDiffSR can restore visual-pleasant images on simulated and real-world remote sensing images, both quantitatively and qualitatively. The code of EDiffSR will be available at https://github.com/XY-boy/EDiffSRComment: Submitted to IEEE TGR

Light-sheet microscopy: a tutorial

This paper is intended to give a comprehensive review of light-sheet (LS) microscopy from an optics perspective. As such, emphasis is placed on the advantages that LS microscope configurations present, given the degree of freedom gained by uncoupling the excitation and detection arms. The new imaging properties are first highlighted in terms of optical parameters and how these have enabled several biomedical applications. Then, the basics are presented for understanding how a LS microscope works. This is followed by a presentation of a tutorial for LS microscope designs, each working at different resolutions and for different applications. Then, based on a numerical Fourier analysis and given the multiple possibilities for generating the LS in the microscope (using Gaussian, Bessel, and Airy beams in the linear and nonlinear regimes), a systematic comparison of their optical performance is presented. Finally, based on advances in optics and photonics, the novel optical implementations possible in a LS microscope are highlighted.Peer ReviewedPostprint (published version