Background estimation and adaptation model with light-change removal for heavily cown-sampled video surveillance signals

This paper describes a background-subtraction system with light change-detection which works on a luminance QCIF-size video signal for surveillance applications. The new proposed pixel background model is controlled by a statistical threshold and is robust for cluttered background and small object motions. Moreover, (or light-change detection, we introduce temporal prediction of pixel values to estimate trends while quickly adapting to scene changes to facilitate a very sensitive detection of moving targets. Experiments show that a local contrast enhancement applied prior to down-sampling improves detection sensitivity, arid combined with the shifted sealed difference and me Wronskian determinant operators provides the best background/foreground detectio

Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient

Non-linear locally-adaptive video contrast enhancement algorithm without artifacts

For real-time imaging with digital video cameras and high-quality display with TV systems, visibility of details is very important to ensure user quality acceptance. Many complex scenes require local contrast improvements that should bring details to the best possible visibility. However, local enhancement methods mainly suffer from ringing artifacts and over-emphasizing noise. We present a multi-window real-time high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Our algorithm controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. The overall quality enhancement is based on a careful selection of the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band. The advantage of the proposed technique is that details in images become more visible without introduction of annoying artifacts. The new scheme can be successfully applied to cameras and TV systems to improve their contrast

Mild intracellular acidification by dexamethasone attenuates mitochondrial dysfunction in a human inflammatory proximal tubule epithelial cell model

Contains fulltext : 177220.pdf (publisher's version ) (Open Access)Septic acute kidney injury (AKI) associates with poor survival rates and often requires renal replacement therapy. Glucocorticoids may pose renal protective effects in sepsis via stimulation of mitochondrial function. Therefore, we studied the mitochondrial effects of dexamethasone in an experimental inflammatory proximal tubule epithelial cell model. Treatment of human proximal tubule epithelial cells with lipopolysaccharide (LPS) closely resembles pathophysiological processes during endotoxaemia, and led to increased cytokine excretion rates and cellular reactive oxygen species levels, combined with a reduced mitochondrial membrane potential and respiratory capacity. These effects were attenuated by dexamethasone. Dexamethasone specifically increased the expression and activity of mitochondrial complex V (CV), which could not be explained by an increase in mitochondrial mass. Finally, we demonstrated that dexamethasone acidified the intracellular milieu and consequently reversed LPS-induced alkalisation, leading to restoration of the mitochondrial function. This acidification also provides an explanation for the increase in CV expression, which is expected to compensate for the inhibitory effect of the acidified environment on this complex. Besides the mechanistic insights into the beneficial effects of dexamethasone during renal cellular inflammation, our work also supports a key role for mitochondria in this process and, hence, provides novel therapeutic avenues for the treatment of AKI

Multi-band locally-adaptive contrast enhancement algorithm with built-in noise and artifact suppression mechanisms

For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer confidence. Usually, image quality is improved by enhancing contrast and sharpness. Many complex scenes require local contrast improvements that should bring details to the best possible visibility. However, local enhancement methods mainly suffer from ringing artifacts and noise over-enhancement. In this paper, we present a new multi-window real-time high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy. Our algorithm simultaneously controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. The overall quality enhancement is based on a careful selection of the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band. The advantage of the proposed technique is that detail gains can be set much higher than usual and the algorithm will reduce them only at places where it is really needed

Enhancement tuning and control for high dynamic range images in multi-scale locally adaptive contrast enhancement algorithms

For real-time imaging in surveillance applications, visibility of details is of primary importance to ensure customer confidence. If we display High Dynamic-Range (HDR) scenes whose contrast spans four or more orders of magnitude on a conventional monitor without additional processing, results are unacceptable. Compression of the dynamic range is therefore a compulsory part of any high-end video processing chain because standard monitors are inherently Low- Dynamic Range (LDR) devices with maximally two orders of display dynamic range. In real-time camera processing, many complex scenes are improved with local contrast enhancements, bringing details to the best possible visibility. In this paper, we show how a multi-scale high-frequency enhancement scheme, in which gain is a non-linear function of the detail energy, can be used for the dynamic range compression of HDR real-time video camera signals. We also show the connection of our enhancement scheme to the processing way of the Human Visual System (HVS). Our algorithm simultaneously controls perceived sharpness, ringing ("halo") artifacts (contrast) and noise, resulting in a good balance between visibility of details and non-disturbance of artifacts. The overall quality enhancement, suitable for both HDR and LDR scenes, is based on a careful selection of the filter types for the multi-band decomposition and a detailed analysis of the signal per frequency band

Real-time local contrast enhancement algorithm for video cameras with built-in noise and artifact suppression

For real-time imaging with digital video cameras, visibility of details is very important to ensure user confidence. Many complex scenes require local contrast improvements that should bring details to the best possible visibility. However, local enhancement methods mainly suffer from ringing artifacts and over-emphasizing noise. We present a multi-window real-time high-frequency enhancement scheme, in which gain is a nonlinear function of the detail energy. Our algorithm controls perceived sharpness, ringing artifacts (contrast) and noise, resulting in a good balance between visibility of details and non- disturbance of artifacts. The new scheme can be successfully applied to any class of video system that would benefit from contrast improvement