In this paper, a modified 3D computational reconstruction method in the compressive 4D-spectro-volumetric snapshot imaging system is proposed for better sensing spectral information of 3D objects. In the design of the imaging system, a microlens array (MLA) is used to obtain a set of multi-view elemental images (EIs) of the 3D scenes. Then, these elemental images with one dimensional spectral information and different perspectives are captured by the coded aperture snapshot spectral imager (CASSI) which can sense the spectral data cube onto a compressive 2D measurement image. Finally, the depth images of 3D objects at arbitrary depths, like a focal stack, are computed by inversely mapping the elemental images according to geometrical optics. With the spectral estimation algorithm, the spectral information of 3D objects is also reconstructed. Using a shifted translation matrix, the contrast of the reconstruction result is further enhanced. Numerical simulation results verify the performance of the proposed method. The system can obtain both 3D spatial information and spectral data on 3D objects using only one single snapshot, which is valuable in the agricultural harvesting robots and other 3D dynamic scenes.
KEYWORDS: 3D image processing, Integral imaging, Microlens array, 3D metrology, Imaging spectroscopy, 3D image reconstruction, Imaging systems, Coded apertures, 3D acquisition, Sensors
In this paper, a compressive spectral integral imaging system using a microlens array (MLA) is proposed. This system can sense the 4D spectro-volumetric information into a compressive 2D measurement image on the detector plane. In the reconstruction process, the 3D spatial information at different depths and the spectral responses of each spatial volume pixel can be obtained simultaneously. In the simulation, sensing of the 3D objects is carried out by optically recording elemental images (EIs) using a scanned pinhole camera. With the elemental images, a spectral data cube with different perspectives and depth information can be reconstructed using the TwIST algorithm in the multi-shot compressive spectral imaging framework. Then, the 3D spatial images with one dimensional spectral information at arbitrary depths are computed using the computational integral imaging method by inversely mapping the elemental images according to geometrical optics. The simulation results verify the feasibility of the proposed system. The 3D volume images and the spectral information of the volume pixels can be successfully reconstructed at the location of the 3D objects. The proposed system can capture both 3D volumetric images and spectral information in a video rate, which is valuable in biomedical imaging and chemical analysis.
Dimensionality reduction is a frequent preprocessing step in hyperspectral image analysis. High-dimensional data will cause the issue of the “curse of dimensionality” in the applications of hyperspectral imagery. In this paper, a dimensionality reduction method of hyperspectral images based on random projection (RP) for target detection was investigated. In the application areas of hyperspectral imagery, e.g. target detection, the high dimensionality of the hyperspectral data would lead to burdensome computations. Random projection is attractive in this area because it is data independent and computationally more efficient than other widely-used hyperspectral dimensionality-reduction methods, such as Principal Component Analysis (PCA) or the maximum-noise-fraction (MNF) transform. In RP, the original highdimensional data is projected onto a low dimensional subspace using a random matrix, which is very simple. Theoretical and experimental results indicated that random projections preserved the structure of the original high-dimensional data quite well without introducing significant distortion. In the experiments, Constrained Energy Minimization (CEM) was adopted as the target detector and a RP-based CEM method for hyperspectral target detection was implemented to reveal that random projections might be a good alternative as a dimensionality reduction tool of hyperspectral images to yield improved target detection with higher detection accuracy and lower computation time than other methods.
Compression is a kernel procedure in hyperspectral image processing due to its massive data which will bring great difficulty in date storage and transmission. In this paper, a novel hyperspectral compression algorithm based on hybrid encoding which combines with the methods of the band optimized grouping and the wavelet transform is proposed. Given the characteristic of correlation coefficients between adjacent spectral bands, an optimized band grouping and reference frame selection method is first utilized to group bands adaptively. Then according to the band number of each group, the redundancy in the spatial and spectral domain is removed through the spatial domain entropy coding and the minimum residual based linear prediction method. Thus, embedded code streams are obtained by encoding the residual images using the improved embedded zerotree wavelet based SPIHT encode method. In the experments, hyperspectral images collected by the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) were used to validate the performance of the proposed algorithm. The results show that the proposed approach achieves a good performance in reconstructed image quality and computation complexity.The average peak signal to noise ratio (PSNR) is increased by 0.21~0.81dB compared with other off-the-shelf algorithms under the same compression ratio.
A sparse representation based multi-threshold segmentation (SRMTS) algorithm for target detection in hyperspectral images is proposed. Benefiting from the sparse representation, the high-dimensional spectral data can be characterized into a series of sparse feature vectors which has only a few nonzero coefficients. Through setting an appropriate threshold, the noise removed sparse spectral vectors are divided into two subspaces in the sparse domain consistent with the sample spectrum to separate the target from the background. Then a correlation and a vector 1-norm are calculated respectively in the subspaces. The sparse characteristic of the target is used to ext ract the target with a multi -threshold method. Unlike the conventional hyperspectral dimensionality reduction methods used in target detection algorithms, like Principal Components Analysis (PCA) and Maximum Noise Fraction (MNF), this algorithm maintains the spectral characteristics while removing the noise due to the sparse representation. In the experiments, an orthogonal wavelet sparse base is used to sparse the spectral information and a best contraction threshold to remove the hyperspectral image noise according to the noise estimation of the test images. Compared with co mmon algorithms, such as Adaptive Cosine Estimator (ACE), Constrained Energy Minimizat ion (CEM) and the noise removed MNF-CEM algorithm, the proposed algorithm demonstrates higher detection rates and robustness via the ROC curves.
In imaging systems based on compressed sensing, error in the
measured data is incurred due to the nonlinear response of the
photo-detector, which affects the quality of reconstructed images.
Conventionally, the affected measured data will be eliminated in order to
obtain the high reconstruction quality. However, when there are too many
measured data affected, it is impossible to reject all such data which will
certainly degrade the imaging efficiency. Therefore, an algorithm of
regionally compensating the non-linear response from the detector is
proposed. The nonlinear measured data will be compensated but not
rejected in the proposed algorithm. According to the detector response
curve, all of the nonlinear measured data will be divided into several parts. The data in the same part will have the same average compensation factor
which can be obtained from the response curve after calculation. The
affected measured data will be compensated with the compensation factor
regionally before used to reconstruct the original image. The theoretical
analysis and simulation results show that a reconstructed image with high
quality can still be obtained even when over 80% of the measured data
are nonlinear. It is impossible to get such a high quality reconstructed
image with the conventionally algorithm. The PSNR and the M-rate in
the simulation show that the compensation algorithm can greatly deal
with the situation of too many nonlinear measured data.
In imaging systems based on compressed sensing, error in the measured data is incurred due to the nonlinear response of the photo detector, which affects the quality of the reconstructed images. We propose an algorithm to compensate the nonlinear response from the detector. The compensation is based on the detector response curve on the measured data. The theoretical analysis and simulation results show that this algorithm can greatly reduce the reconstruction errors caused by the detector’s nonlinear response. Furthermore the peak signal-to-noise ratio of the reconstructed image and the system reconstruction rate have been significantly improved, while the fine feature of the images is better preserved and reconstructed as compared to that without using the algorithm.
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