Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds

Image denoising can be described as the problem of mapping from a noisy image to a noise-free image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with plain multi layer perceptrons (MLP) applied to image patches. We will show that by training on large image databases we are able to outperform the current state-of-the-art image denoising methods. In addition, our method achieves results that are superior to one type of theoretical bound and goes a large way toward closing the gap with a second type of theoretical bound. Our approach is easily adapted to less extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes, for which we achieve excellent results as well. We will show that combining a block-matching procedure with MLPs can further improve the results on certain images. In a second paper, we detail the training trade-offs and the inner mechanisms of our MLPs

Delphi consensus recommendation for optimization of pulmonary hypertension therapy focusing on switching from a phosphodiesterase 5 inhibitor to riociguat

Dual combination therapy with a phosphodiesterase‐5 inhibitor (PDE5i) and endothelin receptor antagonist is recommended for most patients with intermediate‐risk pulmonary arterial hypertension (PAH). The RESPITE and REPLACE studies suggest that switching from a PDE5i to a soluble guanylate cyclase (sGC) activator may provide clinical improvement in this situation. The optimal approach to escalation or transition of therapy in this or other scenarios is not well defined. We developed an expert consensus statement on the transition to sGC and other treatment escalations and transitions in PAH using a modified Delphi process. The Delphi process used a panel of 20 physicians with expertise in PAH. Panelists answered three questionnaires on the management of treatment escalations and transitions in PAH. The initial questionnaire included open‐ended questions. Later questionnaires consolidated the responses into statements that panelists rated on a Likert scale from −5 (strongly disagree) to +5 (strongly agree) to determine consensus. The Delphi process produced several consensus recommendations. Escalation should be considered for patients who are at high risk or not achieving treatment goals, by adding an agent from a new class, switching from oral to parenteral prostacyclins, or increasing the dose. Switching to a new class or within a class should be considered if tolerability or other considerations unrelated to efficacy are affecting adherence. Switching from a PDE5i to an SGC activator may benefit patients with intermediate risk who are not improving on their present therapy. These consensus‐based recommendations may be helpful to clinicians and beneficial for patients when evidence‐based guidance is unavailable

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Modellierungs- und Lernverfahren für Bildentrauschung

Currently, most photographs are taken with digital cameras. Improvements in chip technologies have made possible the integration of digital cameras into other devices, such as mobile phones. This in turn has caused an explosion in the number of digital photographs taken each day. Unfortunately, all digital photographs contain an undesirable component commonly referred to as noise. Noise arises for a number of reasons. For example, photon shot noise is due to the discrete nature of light, and dark-current noise is due to the thermal energy of a camera's sensor. Image denoising is the problem of finding a clean image, given a noisy one. Using a denoising method becomes necessary when modifying the image acquisition process in such a way as to reduce the noise is not an option. This thesis presents three novel contributions to the field of image denoising. Improving existing approaches using a multi-scale meta-procedure. Most denoising algorithms focus on recovering high-frequencies. However, for high noise levels it is also important to recover low-frequencies. We present a multi-scale meta-procedure that applies existing denoising algorithms across different scales and combines the resulting images into a single denoised image. We show that our method can improve the results achieved by many denoising algorithms. Astronomical image denoising with a pixel-specific noise model. For digital photographs of astronomical objects, where exposure times are long, the dark-current noise is a significant source of noise. Usually, denoising methods assume additive white Gaussian noise, with equal variance for each pixel. However, dark-current noise has different properties for every pixel. We use a pixel-speci&#64257;c noise model to handle dark-current noise, as well as an image prior adapted to astronomical images. Our method is shown to perform well in a laboratory environment, and produces visually appealing results in a real-world setting. Image denoising using multi-layer perceptrons. Many of the best-performing denoising methods rely on a cleverly engineered algorithm. In contrast, we take a learning approach to denoising and train a multi-layer perceptron to denoise image patches. Using this approach, we outperform the previous state-of-the-art. Our approach also achieves results that are superior to one type of theoretical bound and goes a large way toward closing the gap with a second type of theoretical bound. Furthermore, we achieve outstanding results on other types of noise, including JPEG-artifacts and Poisson noise. Also, we show that multi-layer perceptrons can be used to combine the results of several denoising algorithms. This approach often yields better results than the best method in the combination. We discuss in detail which trade-offs have to be considered during the training procedure. We are also able to make observations regarding the functioning principle of multi-layer perceptrons for image denoising.Heutzutage werden die meisten fotographischen Bilder mit Digitalkameras aufgenommen. Verfeinerungen der Chip Technologien haben ermöglicht, dass Digitalkameras in andere Geräte so wie Mobiltelefone integriert werden. Dies wiederum hat zu einer Explosion in der Anzahl der täglich aufgenommenen Digitalfotos geführt. Leider enthalten alle Digitalfotos eine unerwünschte Komponente, nämlich das Rauschen. Bildentrauschung ist das Problem, ein sauberes Bild zu &#64257;nden, wenn ein rauschiges gegeben ist. Eine Bildentrauschungsmethode zu verwenden ist dann nötig, wenn es nicht möglich ist, das Bildaufnahmeverfahren so zu verändern, dass weniger Rauschen entsteht. Diese Dissertation präsentiert drei neue Beiträge zu dem Feld der Bildentrauschung. Verbesserung existierender Methoden durch ein multiskalen Metaverfahren: Die meisten Entrauschungsverfahren setzen den Schwerpunkt auf das Wiederherstellen hoher Frequenzen. Allerdings ist es bei starkem Rauschen auch wichtig, niedrigere Frequenzen zu beachten. Wir präsentieren ein multiskalen Metaverfahren, welches existierende Entrauschungsverfahren auf mehreren Skalen anwendet und die jeweiligen Ergebnisse wieder in ein entrauschtes Bild kombiniert. Wir zeigen, dass unser Verfahren die Ergebnisse vieler Entrauschungsverfahren verbessern kann. Entrauschen astronomischer Bilder durch ein pixel-spezifisches Modell des Rauschens: In Digitalbildern von astronomischen Objekten, in welchen die Belichtungszeiten lang sind, ist das Dunkelstromrauschen eine wichtige Quelle von Rauschen. Normalerweise nehmen Entrauschungsverfahren additives, weißes Rauschen, mit gleicher Varianz für jeden Pixel an. Allerdings hat Dunkelstromrauschen andere Eigenschaften für jeden Pixel. Wir benutzen ein pixel-spezi&#64257;sches Modell des Rauschens sowie eine a priori Wahrscheinlichkeit für Bilder, welche an astronomische Bilder angepasst ist. Wir zeigen, dass unsere Methode in einem Laboraufbau gut funktioniert und mit echten Bildern astronomischer Objekte visuell ansprechende Ergebnisse liefert. Entrauschen durch mehrlagige Perzeptronen: Viele der am besten funkionierenden Entrauschungsverfahren verlassen sich auf ausgeklügelt konstruierte Algorithmen. Im Gegensatz dazu benutzen wir einen auf Lernen basierten Ansatz und trainieren mehrlagige Perzeptronen darauf, kleine Bildstücke zu entrauschen. Mit diesem Ansatz ubertreffen wir die Ergebnisse des neuesten Stand der Technik. Unser Ansatz erreicht Ergebnisse, die einer Klasse theoretischer Grenzen uberlegen sind und macht große Schritte, um eine zweite Klasse Grenzen zu erreichen. Außerdem erzielen wir ausgezeichnete Ergebenisse auf anderen Arten von Rauschen, einschließlich JPEG Artefakte und Poisson Rauschen. Wir zeigen auch, dass mehrlagige Perzeptronen in der Lage sind, die Ergebenisse anderer Entrauschungsverfahren zu kombinieren. Dieser Ansatz liefert oft Ergebnisse, die besser als das beste Ergebnis in der Kombination sind. Wir diskutieren im Detail, welche Kompromisse in der Trainingsprozedur eingegangen werden müssen. Wir sind auch in der Lage, Beobachtungen bezüglich der Funkionsweise von mehrlagigen Perzeptronen für Bildentrauschung zu machen

Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling

Most experiments assume a global transit delay time with blood flowing from the tagging region to the imaging slice in plug flow without any dispersion of the magnetization. However, because of cardiac pulsation, nonuniform cross-sectional flow profile, and complex vessel networks, the transit delay time is not a single value but follows a distribution. In this study, we explored the regional effects of magnetization dispersion on quantitative perfusion imaging for varying transit times within a very large interval from the direct comparison of pulsed, pseudo-continuous, and dual-coil continuous arterial spin labeling encoding schemes. Longer distances between tagging and imaging region typically used for continuous tagging schemes enhance the regional bias on the quantitative cerebral blood flow measurement causing an underestimation up to 37 when plug flow is assumed as in the standard model

A machine learning approach for non-blind image deconvolution

Image deconvolution is the ill-posed problem of recovering a sharp image, given a blurry one generated by a convolution. In this work, we deal with space-invariant nonblind deconvolution. Currently, the most successful methods involve a regularized inversion of the blur in Fourier domain as a first step. This step amplifies and colors the noise, and corrupts the image information. In a second (and arguably more difficult) step, one then needs to remove the colored noise, typically using a cleverly engineered algorithm. However, the methods based on this two-step approach do not properly address the fact that the image information has been corrupted. In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network. We will show that this approach outperforms the current state-of-the-art on a large dataset of artificially blurred images. We demonstrate the practical applicability of our method in a real-world example with photographic out-of-focus blur