Even though image signals are typically defined on a regular 2D grid, there also exist many scenarios where this is not the case and the amplitude of the image signal only is available for a non-regular subset of pixel positions. In such a case, a resampling of the image to a regular grid has to be carried out. This is necessary since almost all algorithms and technologies for processing, transmitting or displaying image signals rely on the samples being available on a regular grid. Thus, it is of great importance to reconstruct the image on this regular grid, so that the reconstruction comes closest to the case that the signal has been originally acquired on the regular grid. In this paper, Frequency Selective Reconstruction is introduced for solving this challenging task. This algorithm reconstructs image signals by exploiting the property that small areas of images can be represented sparsely in the Fourier domain. By further considering the basic properties of the optical transfer function of imaging systems, a sparse model of the signal is iteratively generated. In doing so, the proposed algorithm is able to achieve a very high reconstruction quality, in terms of peak signal-to-noise ratio (PSNR) and structural similarity measure as well as in terms of visual quality. The simulation results show that the proposed algorithm is able to outperform state-of-the-art reconstruction algorithms and gains of more than 1 dB PSNR are possible.

Up to now, an increase in camera resolution required image sensors with more and more pixels. However, acquisition systems are limited in their pixels per second throughput given as power and complexity constraints. Simply capturing more pixels in a given system is often not possible. We propose a new non-regular imaging architecture that samples only few pixels and reconstructs a high resolution image afterwards. Our sampling is optimized to provide non-regular spatial sampling from a sensor with regular readout circuits. An existing slow image acquisition system can then be used to capture the data. The image reconstruction is performed with a local sparsity-based approach. The result is a high resolution image that requires a much smaller effort during acquisition

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BackgroundGiven the unreliable self-report in patients with dementia, pain assessment should also rely on the observation of pain behaviors, such as facial expressions. Ideal observers should be well trained and should observe the patient continuously in order to pick up any pain-indicative behavior; which are requisitions beyond realistic possibilities of pain care. Therefore, the need for video-based pain detection systems has been repeatedly voiced. Such systems would allow for constant monitoring of pain behaviors and thereby allow for a timely adjustment of pain management in these fragile patients, who are often undertreated for pain.MethodsIn this road map paper we describe an interdisciplinary approach to develop such a video-based pain detection system. The development starts with the selection of appropriate video material of people in pain as well as the development of technical methods to capture their faces. Furthermore, single facial motions are automatically extracted according to an international coding system. Computer algorithms are trained to detect the combination and timing of those motions, which are pain-indicative.Results/conclusionWe hope to encourage colleagues to join forces and to inform end-users about an imminent solution of a pressing pain-care problem. For the near future, implementation of such systems can be foreseen to monitor immobile patients in intensive and postoperative care situations.

We present a new method for capturing high dynamic range video (HDRV). Our method is based on spatially varying exposures, where individual pixels are covered with filters for different optical attenuation. For preventing the loss in resolution we use a new non- regular arrangement of the attenuation pattern. Subsequent image reconstruction based on the sparsity assumption allows the recon- struction of natural images with high detail. Index Terms High Dynamic Range Image Sensor, Digital Camera, Resolution Enhancement, Sparsit