Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultane...
详细信息
ISBN:
(纸本)9783031167881;9783031167874
Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3 x 3, 4 x 4 or 5 x 5 mosaic), opening up a wide range of applications. Examples include intraoperative imaging, agricultural field inspection and food quality assessment. To capture images across a wide spectrum range, i.e. to achieve high spectral resolution, the sensor design sacrifices spatial resolution. With increasing mosaic size, this effect becomes increasingly detrimental. Furthermore, demosaicing is challenging. Without incorporating edge, shape, and object information during interpolation, chromatic artifacts are likely to appear in the obtained images. Recent approaches use neural networks for demosaicing, enabling direct information extraction from image data. However, obtaining training data for these approaches poses a challenge as well. This work proposes a parallel neural network based demosaicing procedure trained on a new ground truth dataset captured in a controlled environment by a hyperspectral snapshot camera with a 4 x 4 mosaic pattern. The dataset is a combination of real captured scenes with images from publicly available data adapted to the 4 x 4 mosaic pattern. To obtain real world ground-truth data, we performed multiple camera captures with 1-pixel shifts in order to compose the entire data cube. Experiments show that the proposed network outperforms state-of-art networks.
In this fast communication, we examine non-parametric data-adaptive spatial spectrum estimation using an array of sensors. We derive a forward-backward (FB) version of the recent spatial APES (SAPES) beamformer, and s...
详细信息
In this fast communication, we examine non-parametric data-adaptive spatial spectrum estimation using an array of sensors. We derive a forward-backward (FB) version of the recent spatial APES (SAPES) beamformer, and show that the SAPES algorithm is robust to the case of pairwise coherent sources. Numerical simulations indicate that the proposed FB version of SAPES offers higher resolution than the forward-only SAPES algorithm. (c) 2005 Elsevier B.V. All rights reserved.
暂无评论