Extinction controlled adaptive phase-mask coronagraph

Context. Phase-mask coronagraphy is advantageous in terms of inner working angle and discovery space. It is however still plagued by drawbacks such as sensitivity to tip-tilt errors and chromatism. A nulling stellar coronagraph based on the adaptive phase-mask concept using polarization interferometry is presented in this paper. Aims. Our concept aims at dynamically and achromatically optimizing the nulling efficiency of the coronagraph, making it more immune to fast low-order aberrations (tip-tilt errors, focus, ...). Methods. We performed numerical simulations to demonstrate the value of the proposed method. The active control system will correct for the detrimental effects of image instabilities on the destructive interference. The mask adaptability both in size, phase and amplitude also compensates for manufacturing errors of the mask itself, and potentially for chromatic effects. Liquid-crystal properties are used to provide variable transmission of an annulus around the phase mask, but also to achieve the achromatic {\pi} phase shift in the core of the PSF by rotating the polarization by 180 degrees. Results. We developed a new concept and showed its practical advantages using numerical simulations. This new adaptive implementation of the phase-mask coronagraph could advantageously be used on current and next-generation adaptive optics systems, enabling small inner working angles without compromising contrast.Comment: 7 pages, 6 figure

Hyperpixels: Flexible 4D over-segmentation for dense and sparse light fields

4D Light Field (LF) imaging, since it conveys both spatial and angular scene information, can facilitate computer vision tasks and generate immersive experiences for end-users. A key challenge in 4D LF imaging is to flexibly and adaptively represent the included spatio-angular information to facilitate subsequent computer vision applications. Recently, image over-segmentation into homogenous regions with perceptually meaningful information has been exploited to represent 4D LFs. However, existing methods assume densely sampled LFs and do not adequately deal with sparse LFs with large occlusions. Furthermore, the spatio-angular LF cues are not fully exploited in the existing methods. In this paper, the concept of hyperpixels is defined and a flexible, automatic, and adaptive representation for both dense and sparse 4D LFs is proposed. Initially, disparity maps are estimated for all views to enhance over-segmentation accuracy and consistency. Afterwards, a modified weighted K-means clustering using robust spatio-angular features is performed in 4D Euclidean space. Experimental results on several dense and sparse 4D LF datasets show competitive and outperforming performance in terms of over-segmentation accuracy, shape regularity and view consistency against state-of-the-art methods.info:eu-repo/semantics/publishedVersio