Estimation of daylight spectral power distribution from uncalibrated hyperspectral radiance images

This paper introduces a novel framework for estimating the spectral power distribution of daylight illuminants in uncalibrated hyperspectral images, particularly beneficial for dronebased applications in agriculture and forestry. The proposed method uniquely combines image-dependent plausible spectra with a database of physically possible spectra, utilizing an image-independent principal component space (PCS) for estimations. This approach effectively narrows the search space in the spectral domain and employs a random walk methodology to generate spectral candidates, which are then intersected with a pre-trained PCS to predict the illuminant. We demonstrate superior performance compared to existing statistics-based methods across various metrics, validating the framework’s efficacy in accurately estimating illuminants and recovering reflectance values from radiance data. The method is validated within the spectral range of 382–1002 nm and shows potential for extension to broader spectral ranges.Universidad de GranadaNorges Teknisk-Naturvitenskapelige UniversitetCubert Gmb

NIH Workshop 2018: Towards Minimally Invasive or Noninvasive Approaches to Assess Tissue Oxygenation Pre- and Post-transfusion

Because blood transfusion is one of the most common therapeutic interventions in hospitalized patients, much recent research has focused on improving the storage quality in vitro of donor red blood cells (RBCs) that are then used for transfusion. However, there is a significant need for enhancing our understanding of the efficacy of the transfused RBCs in vivo. To this end, the NIH sponsored a one-and-a-half-day workshop that brought together experts in multiple disciplines relevant to tissue oxygenation (eg, transfusion medicine, critical care medicine, cardiology, neurology, neonatology and pediatrics, bioengineering, biochemistry, and imaging). These individuals presented their latest findings, discussed key challenges, and aimed to identify opportunities for facilitating development of new technologies and/or biomarker panels to assess tissue oxygenation in a minimally-invasive to non-invasive fashion, before and after RBC transfusion

NIH Workshop 2018: Towards Minimally-invasive or Non-invasive Approaches to Assess Tissue Oxygenation Pre- and Post-Transfusion

Because blood transfusion is one of the most common therapeutic interventions in hospitalized patients, much recent research has focused on improving the storage quality in vitro of donor red blood cells (RBCs) that are then used for transfusion. However, there is a significant need for enhancing our understanding of the efficacy of the transfused RBCs in vivo. To this end, the NIH sponsored a one-and-a-half-day workshop that brought together experts in multiple disciplines relevant to tissue oxygenation (e.g., transfusion medicine, critical care medicine, cardiology, neurology, neonatology and pediatrics, bioengineering, biochemistry, and imaging). These individuals presented their latest findings, discussed key challenges, and aimed to construct recommendations for facilitating development of new technologies and/or biomarker panels to assess tissue oxygenation in a minimally-invasive to non-invasive fashion, before and after RBC transfusion. The workshop was structured into four sessions: (1) Global Perspective; (2) Organ Systems; (3) Neonatology; and (4) Emerging Technologies. The first day provided an overview of current approaches in the clinical setting, both from a global perspective, including the use of metabolomics for studying RBCs and tissue perfusion, and from a more focused perspective, including tissue oxygenation assessments in neonates and in specific adult organ systems. The second day focused on emerging technologies, which could be applied pre- and post-RBC transfusion, to assess tissue oxygenation in minimally-invasive or non-invasive ways. Each day concluded with an open-microphone discussion among the speakers and workshop participants. The workshop presentations and ensuing interdisciplinary discussions highlighted the potential of technologies to combine global “omics” signatures with additional measures (e.g., thenar eminence measurements or various imaging methods) to predict which patients could potentially benefit from a RBC transfusion and whether the ensuing RBC transfusion was effective. The discussions highlighted the need for collaborations across the various disciplines represented at the meeting to leverage existing technologies and to develop novel approaches for assessing RBC transfusion efficacy in various clinical settings. Although the Workshop took place in April, 2018, the concepts described and the ensuing discussions were, perhaps, even more relevant in April, 2020, at the time of writing this manuscript, during the explosive growth of the COVID-19 pandemic in the United States. Thus, issues relating to maintaining and improving tissue oxygenation and perfusion are especially pertinent because of the extensive pulmonary damage resulting from SARS-CoV-2 infection [1], compromises in perfusion caused by thrombotic-embolic phenomena [2], and damage to circulating RBCs, potentially compromising their oxygen-carrying capacity [3]. The severe end organ effects of SARS-CoV-2 infection mandate even more urgency for improving our understanding of tissue perfusion and oxygenation, improve methods for measuring and monitoring them, and develop novel ways of enhancing them

Réduction de dimensionalité et saillance pour la visualisation d'images spectrales

Nowadays, digital imaging is mostly based on the paradigm that a combinations of a small number of so-called primary colors is sufficient to represent any visible color. For instance, most cameras usepixels with three dimensions: Red, Green and Blue (RGB). Such low dimensional technology suffers from several limitations such as a sensitivity to metamerism and a bounded range of wavelengths. Spectral imaging technologies offer the possibility to overcome these downsides by dealing more finely withe the electromagnetic spectrum. Mutli-, hyper- or ultra-spectral images contain a large number of channels, depicting specific ranges of wavelength, thus allowing to better recover either the radiance of reflectance of the scene. Nevertheless,these large amounts of data require dedicated methods to be properly handled in a variety of applications. This work contributes to defining what is the useful information that must be retained for visualization on a low-dimensional display device. In this context, subjective notions such as appeal and naturalness are to be taken intoaccount, together with objective measures of informative content and dependency. Especially, a novel band selection strategy based on measures derived from Shannon’s entropy is presented and the concept of spectral saliency is introduced.De nos jours, la plupart des dispositifs numériques d’acquisition et d’affichage d’images utilisent un petit nombre de couleurs dites primaires afin de représenter n’importe quelle couleur visible. Par exemple, la majorité des appareils photos "grand public" quantifient la couleur comme une certaine combinaison de Rouge, Vert et Bleu(RVB). Ce genre de technologie est qualifiée de tri-chromatique et, au même titre que les modèles tetra-chromatiques communs en imprimerie, elle présente un certain nombre d’inconvénients, tels que le métamérisme ou encore la limitation aux longueurs d’onde visibles. Afin de palier à ces limitations, les technologies multi-, hyper,voire ultra-spectrale ont connu un gain notable d’attention depuis plusieurs décennies. Un image spectrale est constituée d’un nombre de bandes (ou canaux) supérieur à 3, représentant des régions spectrales spécifiques et permettant de recouvrer la radiance ou reflectance d’objets avec plus de précision et indépendamment du capteur utilisé. De nombreux travaux de recherche ont fait considérablement progresser les méthodes d’acquisition et d’analyse, mais beaucoup de challenges demeurent, particulièrement en ce qui concernel a visualisation de ce type de données. En effet, si une image contient plusieurs dizaines de canaux comment la représenter sur un écran qui n’en accepte que trois ? Dans cette thèse, nous présentons un certain nombre de méthodes d’extraction d’attributs pour l’analyse d’images spectrales, avec une attention particulière sur la problématique de la visualisation

Spectral Printing with a CMYKRGB Printer: A Closer Look

With the advent of multi-channel technologies, printers offer more and more possibilities for spectral reproduction. In order to print a specific color sensation, there are now more degrees of freedom when it comes to combining inks (i.e. more metamerism). In this paper, we take the example of a CMYKRGB printer and propose to visualize the extent of its spectral variability (or degree of metamerism) through the analysis of so-called paramermismatch gamuts. We then evaluate the suitability of the recently proposed LabAB interim connection space in the design of look-up tables for spectral color management. We demonstrate in particular that the spectral variability of this printer is small enough to drastically reduce the number of necessary grid points needed to sample the connection space without loss of perceived quality

Evidence of change blindness in subjective image fidelity assessment

Change blindness is a striking phenomenon which basically means that we can look without seeing. It originates from a faulty communication between early vision (the eye) and visual working memory (the brain). In this paper, we present evidence that this faulty communication needs to be accounted for in image fidelity assessment (also known as full-reference image quality assessment). We designed a user study to analyse participants opinions based on how much they have to rely on their visual working memory in order to give fidelity score. Results demonstrate that significantly more severe judgments were made when reliance on visual short-term memory was minimal, suggesting limitations in the observers' ability to notice image differences in the typical pairwise comparison setup. Furthermore, a comparison of the efficiency of six state-of-the-art image fidelity assessment models (so-called metrics) reveals that five of them perform significantly better at predicting results obtained when reliance on memory is minimal

A Three-Feature Model to Predict Colour Change Blindness

Change blindness is a striking shortcoming of our visual system which is exploited in the popular ‘Spot the difference’ game, as it makes us unable to notice large visual changes happening right before our eyes. Change blindness illustrates the fact that we see much less than we think we do. In this paper, we introduce a fully automated model to predict colour change blindness in cartoon images based on image complexity, change magnitude and observer experience. Using linear regression with only three parameters, the predictions of the proposed model correlate significantly with measured detection times. We also demonstrate the efficacy of the model to classify stimuli in terms of difficulty