Compressed coded aperture based imaging warning system with a low resolution optical sensor is proposed in this paper, which is specifically designed to support the demands of rapid, high resolution, long-range detection and warning in complex battlefield environment. After analyzing of the tactic specification and the technical specification, the key techniques of this novel alarming system are discussed and designed, including optical imaging module, image-processing module, alarming control module and interfaces unit. The optical imaging module is used for image compression, then, the coded image will be mathematically reconstructed to a high resolution image by the image-processing module. The presented super-resolution reconstruction algorithm is efficient and robust. Combining compressed coded imaging simulation and coded image super-resolution reconstruction, experiments show that the compressed coded aperture imaging alarming system has a longer detectable range and higher resolution, which is potential in the defence of important targets.
Satellite imagery always has low-resolution causing poor application in practice because the serious degradation in imaging is resulted in many factors such as atmospheric turbulence, cloud, and aberration of optical system. To reconstruct the degraded remote sensing images with a high quality, we designed an algorithm to estimate the system modulation transfer function (MTF) accurately. Phase congruency is employed to detect the edges and corners of the image first, then the significant edges, which are utilized to estimate the edge spread function (ESF) using inclined edge method, are picked up from above features through a certain line detection measurement. An image restoration algorithm based on total variation (TV) is introduced to deconvolute the degraded image with the estimated MTF which is derived from the ESF. The experiments show that this method is adaptive and efficient to recover the remote sensing images taken from a Chinese Satellite. The restored images with a higher resolution and higher signal-to-noise ratio (SNR) will improve the applications greatly.
Proc. SPIE. 8877, Unconventional Imaging and Wavefront Sensing 2013
KEYWORDS: Signal to noise ratio, Optical sensors, Super resolution, Imaging systems, Image restoration, Image resolution, Spatial light modulators, Optical resolution, Reconstruction algorithms, Simulation of CCA and DLA aggregates
The classical methods of compressed coded aperture (CCA) still require an optical sensor with high resolution, although the sampling rate has broken the Nyquist sampling rate already. A novel architecture of multi-shot compressed coded aperture imaging (MCCAI) using a low resolution optical sensor is proposed, which is mainly based on the 4-f imaging system, combining with two spatial light modulators (SLM) to achieve the compressive imaging goal. The first SLM employed for random convolution is placed at the frequency spectrum plane of the 4-f imaging system, while the second SLM worked as a selecting filter is positioned in front of the optical sensor. By altering the random coded pattern of the second SLM and sampling, a couple of observations can be obtained by a low resolution optical sensor easily, and these observations will be combined mathematically and used to reconstruct the high resolution image. That is to say, MCCAI aims at realizing the super resolution imaging with multiple random samplings by using a low resolution optical sensor. To improve the computational imaging performance, total variation (TV) regularization is introduced into the super resolution reconstruction model to get rid of the artifacts, and alternating direction method of multipliers (ADM) is utilized to solve the optimal result efficiently. The results show that the MCCAI architecture is suitable for super resolution computational imaging using a much lower resolution optical sensor than traditional CCA imaging methods by capturing multiple frame images.
Proc. SPIE. 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications
KEYWORDS: Signal to noise ratio, Super resolution, Image compression, Image processing, Image restoration, Image resolution, Reconstruction algorithms, Optimization (mathematics), Coded aperture imaging, Simulation of CCA and DLA aggregates
The compressed coded aperture imaging method based on Compressed Sensing theory is developed to acquire high resolution image from a low resolution Focal Plane Array (FPA) device using Super Resolution (SR) reconstruction algorithms, which makes it possible to sample fewer points and reconstruct high resolution images. However, a lot of problems remain unsolved in this field. Aiming at realizing the super resolution imaging with multiple random samplings by using a low resolution optical sensor, a novel architecture of multi-shot compressed coded aperture imaging (MCCAI) is proposed in the paper. Based on the classical ℓ<sub>2</sub> -ℓ<sub>1</sub> optimization model, the high frequency information of the reconstructed image is reserved. Although the low-frequency components which should be smooth are mixed with high frequency components, which is displayed as the artifacts that arise in the process of the image reconstruction. With the purpose of solving this problem, a regular term of total variation is appended to the original optimization. The improved ℓ<sub>2</sub> -ℓ<sub>1</sub> -TV optimization model can save the high frequency of the scene in the largest degree, and at the same time reduce the artifacts, which can dramatically improve the quality of the reconstructed image. Using the two optimization model, three different images are tested, and the experimental results show that comparing with the ℓ<sub>2</sub> -ℓ<sub>1</sub> optimization model, the ℓ<sub>2</sub> -ℓ<sub>1</sub> -TV optimization model can improve the image quality of the compressed coded aperture effectively and eliminate the artifacts while retaining the original information of the signals and improving the SNR (signal-to-noise ratio).
As an important photovoltaic detector in the night vision imaging systems, some main performance parameters decide
the properties of the low illuminance CCDs greatly including noise, quantum effects, dynamic range and dark current，
and it is necessary to design a measurement system to measure the performance parameters of the low illuminance CCD.
This article designs a set of low illuminance CCD chips’ performance parameter measurement system, which is consisted
of five parts including adjustable monochromatic light source, integrating sphere-darkroom, Dewar control chamber,
main control circuit and the master computer software for automatic measurement. By persistent demonstration, the
performance parameters measurement system which is focused on the low illuminance CCD proposed in this paper has
the advantages of compact, good compatibility, theoretical measurement precision and fully automated measurement
etc.The appropriate equipment and instruments are selected in this measurement system. And the connections of each
subsystem are designed independently, which guarantees the tightness of the total system, eliminate the effects of stray
light at the same time and improves the measurement accuracy of the system. Besides, this measurement system solves
the generation of monochromatic light, and the measurement of low illuminance CCDs at a low temperature.