Spectral video is crucial for monitoring of dynamic scenes, reconnaissance of moving targets, observation and tracking
of living cells, etc. The traditional spectral imaging methods need multiple exposures to capture a full frame spectral
image, which leads to a low temporal resolution and thus lose their value as spectral video. The new code aperture
snapshot spectral imaging (CASSI) method has been emerging in recent years, which is suitable for spectral video
acquisition, due to its high-speed snapshot and few-amount measurements. Based on the CASSI, this paper proposes a
compressive spectral video acquisition method with double-channel complementary coded aperture. The method can
achieve the spectral video with a high temporal resolution by directly sampling the 3D spectral scene with 2D array
sensor in only one snapshot. Furthermore, by using the double-channel complementary coded aperture in compressive
measurement and the sparse regularity in the optimization recovery together, we can obtain the higher PSNR and better
visual effects compared with the single-channel CASSI. Simulation results demonstrate the efficacy of the proposed
method.
By the success of compressive sensing (CS), coded aperture snapshot spectral imager (CASSI) computationally
obtains 3D spectral images from 2D compressive measurement. In CASSI, each pixel of the detector captures
spectral information only from one voxel in each band with binary weights (i.e., 0 or 1), which limits the variety
of superposition relationship among the 3D voxels in the underlying scene. Moreover, the correspondence of each
pixel of detector to each pixel of coded aperture cannot be readily achieved in the presence of dispersive prism,
due to the small pixel sizes of these elements (often in micrometer). In this paper, we propose a flexible design to
improve the performance of CASSI with currently employed optical elements in CASSI. Specifically, the proposed
design integrates a kind of flexible alignment relationship along the coded aperture, the dispersive prism and
the detector. Each measurement of the detector is manifested as the summation of several voxels in each band
with random decimal weights and different measurements corresponds to overlapped voxels, which provides more
sufficient superposition relationship of the scene information. This flexible design favors the sensing mechanism
better satisfy the requirement of CS theory. Furthermore, the proposed design greatly reduces the alignment
complexity and burden of system construction. Preliminary result achieves improved image quality, including
higher PSNR and better perceptual effect, compared to the traditional design.
For multispectral image acquisition in remote sensing, high spatial resolution requires a small instantaneous field of view (IFOV). However, the smaller the IFOV, the lower the amount of light exposure to imaging sensors, and the lower the signal-to-noise ratio. To overcome this weakness, we propose a new random coded exposure technique for acquiring high-resolution multispectral images without reducing IFOV. The new image acquisition system employs a high-speed rotating mirror controlled by a random sequence to modulate exposure to an ordinary imager without increasing the sampling rate. The proposed high-speed coded exposure strategy makes it possible to maintain sufficient light exposure even with a small IFOV. The randomly sampled multispectral image can be recovered in high spatial resolution by exploiting the signal sparsity. The recovery algorithm is based on the compressive sensing theory. Simulation results demonstrate the efficacy of the proposed technique.
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