Refine
Year of publication
Document Type
- Article (536)
- Part of a Book (409)
- Conference Proceeding (407)
- Workingpaper / Report (78)
- Book (67)
- Article trade magazine (64)
- Collection (41)
- Doctoral Thesis (14)
- Patent (12)
- Contribution to a Periodical (6)
Language
- German (923)
- English (717)
- French (11)
- Russian (6)
- Multiple languages (3)
Has Fulltext
- no (1660) (remove)
Keywords
- Forena (94)
- VSVR (48)
- Innovation (44)
- Design Thinking (33)
- Innovationsmethoden (33)
- Kreativitätstechniken (33)
- Workshop (33)
- FHD (25)
- Marketing (22)
- Virtual (TV) Studio (22)
Department/institution
- Fachbereich - Sozial- & Kulturwissenschaften (500)
- Fachbereich - Medien (414)
- Fachbereich - Maschinenbau und Verfahrenstechnik (356)
- Fachbereich - Wirtschaftswissenschaften (193)
- Creative Media Production and Entertainment Computing (151)
- Fachbereich - Elektro- & Informationstechnik (78)
- Fachbereich - Architektur (56)
- Sound and Vibration Engineering (45)
- Digitale Vernetzung und Informationssicherheit (33)
- Hochschulbibliothek (23)
Ertragsteuern
(2019)
IAS 12: Ertragsteuern
IFRIC 21: Abgaben
IFRIC 23: Unsicherheit bezüglich der ertragsteuerlichen Behandlung
SIC‐10: Beihilfen der öffentlichen Hand — Kein spezifischer Zusammenhang mit betrieblichen Tätigkeiten
SIC‐25: Ertragsteuern — Änderungen im Steuerstatus eines Unternehmens oder seiner Eigentümer
Beizulegender Zeitwert
(2019)
Ausgewählte Angaben
(2019)
Anteilsbasierte Vergütung
(2019)
Finanzinstrumente
(2019)
IAS 32: Finanzinstrumente – Darstellung
IAS 39: Finanzinstrumente: Ansatz und Bewertung
IFRS 7: Finanzinstrumente - Angaben
IFRS 9: Finanzinstrumente
IFRIC 2: Geschäftsanteile an Genossenschaften und ähnliche Instrumente
IFRIC 5: Rechte auf Anteile an Fonds für Entsorgung, Rekultivierung und Umweltsanierung
IFRIC 16: Absicherung einer Nettoinvestition in einen ausländischen Geschäftsbetrieb
IFRIC 17: Sachdividenden an Eigentümer
IFRIC 19: Tilgung finanzieller Verbindlichkeiten durch Eigenkapitalinstrumente
Executive Assessment
(2015)
Wirtschaftsprivatrecht
(2016)
Internationales Management
(2017)
Image restoration is a process used to remove blur (from different sources like object motion or aberrations) from images by either non-blind or blind-deconvolution. The metrics commonly used to quantify the restoration process are peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Often only a small sample of test images are used (like Lena or the camera guy). In optical design research PSNR and SSIM are not normally used, here image quality metrics based on linear system theory (e.g. modulation transfer function, MTF) are used to quantify optical errors like spherical or chromatic aberration. In this article we investigate how different image restoration algorithms can be quantified by applying image quality metrics. We start with synthetic image data that is used in camera test stands (e.g. Siemens star etc.), apply two different spatially variant degradation algorithms, and restore the original image by a direct method (Wiener filtering within sub-images), and by an iterative method (alternating direction method of multipliers, ADMM). Afterwards we compare the quality metrics (like MTF curves) for the original, the degraded and the restored image. As a first result we show that restoration algorithms sometimes fail in dealing with non-natural scenes, e.g. slanted-edge targets. Further, these first results indicate a correlation between degradation and restoration, i.e. the restoration algorithms are not capable of removing the optically relevant errors introduced by the degradation, a fact neither visible nor available from the PSNR values. We discuss the relevance in the context of the automotive industry, where image restoration may yield distinct advantages for camera-based applications, but testing methods rely on the used image quality metrics.
We had already demonstrated a numerical model for the point spread function (PSF) of an optical system that can efficiently model both the experimental measurements and the lens design simulations of the PSF. The novelty lies in the portability and the parameterization of this model, which allow for completely new ways to validate optical systems, which is especially interesting not only for mass production optics such as in the automotive industry but also for ophthalmology. The numerical basis for this model is a nonlinear regression of the PSF with an artificial neural network (ANN). After briefly describing both the principle and the applications of the model, we then discuss two optically important aspects: the spatial resolution and the accuracy of the model. Using mean squared error (MSE) as a metric, we vary the topology of the neural network, both in the number of neurons and in the number of hidden layers. Measurement and simulation of a PSF can have a much higher spatial resolution than the typical pixel size used in current camera sensors. We discuss the influence this has on the topology of the ANN. The relative accuracy of the averaged pixel MSE is below 10 − 4, thus giving confidence that the regression does indeed model the measurement data with good accuracy. This article is only the starting point, and we propose several research avenues for future work.