Denoising in optical coherence tomography volumes for improved 3D visualization.

Optical coherence tomography (OCT) has already become one of the most important diagnostic tools in different fields of medicine, as well as in various industrial applications. The most important characteristic of OCT is its high resolution, both in depth and the transverse direction. Together with the information on the tissue density, OCT offers highly precise information on tissue geometry.

Adaptive point cloud acquisition and upsampling for automotive lidar.

One of the crucial factors in achieving a higher level of autonomy of self-driving vehicles is a sensor capable of acquiring accurate and robust information about the environment and other participants in traffic. In the past few decades, various types of sensors have been used for this purpose, such as cameras registering visible, near-infrared, and thermal parts of the spectrum, as well as radars, ultrasonic sensors, and lidar. Due to their high range, accuracy, and robustness, lidars are gaining popularity in numerous applications.

Efficient Training Procedures for Multi-Spectra Demosaicing.

The simultaneous acquisition of multi-spectral images on a single sensor can be efficiently performed by single shot capture using a mutli-spectral filter array. This paper focused on the demosaicing of color and near-infrared bands and relied on a convolutional neural network (CNN). To train the deep learning model robustly and accurately, it is necessary to provide enough training data, with sufficient variability.

Deep Visible and Thermal Image Fusion for Enhanced Pedestrian Visibility.

Reliable vision in challenging illumination conditions is one of the crucial requirements of future autonomous automotive systems. In the last decade, thermal cameras have become more easily accessible to a larger number of researchers. This has resulted in numerous studies which confirmed the benefits of the thermal cameras in limited visibility conditions.

The Effect of the Color Filter Array Layout Choice on State-of-the-Art Demosaicing.

Interpolation from a Color Filter Array (CFA) is the most common method for obtaining full color image data. Its success relies on the smart combination of a CFA and a demosaicing algorithm. Demosaicing on the one hand has been extensively studied.

Semiautomatic Epicardial Fat Segmentation Based on Fuzzy c-Means Clustering and Geometric Ellipse Fitting.

Automatic segmentation of particular heart parts plays an important role in recognition tasks, which is utilized for diagnosis and treatment. One particularly important application is segmentation of epicardial fat (surrounds the heart), which is shown by various studies to indicate risk level for developing various cardiovascular diseases as well as to predict progression of certain diseases. Quantification of epicardial fat from CT images requires advance image segmentation methods.

Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras.

In this paper we present a new denoising method for the depth images of a 3D imaging sensor, based on the time-of-flight principle. We propose novel ways to use luminance-like information produced by a time-of flight camera along with depth images. Firstly, we propose a wavelet-based method for estimating the noise level in depth images, using luminance information.