In this paper, we propose a novel way of using time-of-flight camera depth and amplitude images to reduce the noise in depth images with prior knowledge of spatial noise distribution, which is correlated with the incident light falling on each pixel. The denoising is done in wavelet space and the influence and implications of the extended noise model to wavelet space and common denoising methods are shown.

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In this paper we present two novel methods for initializing region-based active contours. First we use an analysis of higher order statistic attributes of the complete image and the regions of the initial contour candidates. Then we extend this approach by taking the dissimilarity between the initialized regions and the complete image into account. We compare our method with the standard initialization of active contours for different types and degree of noise in synthetic and real images. It is shown that our method outperforms state of the art initializations of active contours considering the total accuracy and Cohen's kappa.

In this paper we present a new compressed sensing model and reconstruction method for multi-detector signal acquisition. We extend the concept of the famous single-pixel camera to a multidetector device with the benefit of reducing measurement time, while still providing resolution enhancement and deblurring. We provide a scalable model which allows the trade off between system complexity (number of detectors) and time (number of measurements).We test our model on simulated sparse and compressible data and show convergence using the proposed reconstruction method. We also show that our model allows for significant reduction of necessary measurements.A real-live setup for data acquisition according to the new model is presented and we show successful reconstruction of the acquired data. With this setup it is possible to acquire super-resolution images with a low resolution camera. The measurements can also be corrupted by a considerable amount of blurring and noise.