Compressive sensing (CS) is a signal processing technique that enables a signal that has a sparse representation in a known basis to be reconstructed using measurements obtained below the Nyquist rate. Single detector image reconstruction applications using CS have been shown to give promising results. In this study, we investigate the application of CS theory to single detector infrared (IR) rosette scanning systems which suffer from low performance compared to costly focal plane array (FPA) detectors. The single detector pseudoimaging rosette scanning system scans the scene with a specific pattern and performs processing to estimate the target location without forming an image. In this context, this generation of scanning systems may be improved by utilizing the samples obtained by the rosette scanning pattern in conjunction with the CS framework. For this purpose, we consider surface-to-air engagement scenarios using IR images containing aerial targets and flares. The IR images have been reconstructed from samples obtained with the rosette scanning pattern and other baseline sampling strategies. It has been shown that the proposed scheme exhibits good reconstruction performance and a large size FPA imaging performance can be achieved using a single IR detector with a rosette scanning pattern.
Compressive sensing (CS) theory states that a signal which can be sparsely represented in a known basis may be reconstructed from its samples which have been obtained below the Nyquist rate. Image reconstruction with a single detector using CS theory has been shown to give promising results. In this work, we investigate the application of CS theory to single detector infrared (IR) rosette scanning systems. The single detector pseudo-imaging rosette scanning system scans the scene with a specific pattern and performs processing to estimate the target location without forming an image. These systems suffer from low performance compared to costly focal plane array (FPA) detectors. Using the CS framework, these scanning systems may be improved by reconstructing the samples obtained by the rosette scanning pattern. For this purpose, we consider surface to air engagement scenarios where the IR images contain aerial targets and flares. The IR images have been reconstructed from samples obtained with the rosette scanning pattern and other baseline sampling strategies. It has been shown that the proposed scheme exhibits good reconstruction performance and large size FPA imaging performance can be achieved using a single IR detector with a rosette scanning pattern.
In this paper, we investigate the application of compressive sensing theory to single detector infrared seekers. Compressive sensing is a novel signal processing technique which enables a compressible signal to be constructed using fewer measurements obtained in a specific way below the Nyquist rate. Single detector image reconstruction applications using compressive sensing have been shown to be successful. Infrared seekers utilizing single detectors suffer from low performance compared to costly focal plane array detectors. The single detector, pseudo-imaging rosette scanning seekers scan the scene with a specific pattern and process the resultant signal with signal processing methods to estimate the target location without forming an image. In this context, this type of old generation seekers can be converted to imaging systems by utilizing the samples obtained by the scanning pattern in conjunction with the compressive sensing theory framework. In this study, infrared images have been reconstructed from samples obtained by the rosette scanning pattern for different sample numbers and it has been shown that the results obtained are comparable to the results obtained by other sampling methods proposed in the literature.
The compressive sensing framework states that a signal which has sparse representation in a known basis may be reconstructed from samples obtained from a sub-Nyquist sampling rate. Due to its inherent properties, the Fourier domain is widely used in compressive sensing applications. Sparse signal recovery applications making use of a small number of Fourier Transform coe±cients have made solutions to large scale data recovery problems, i.e. images, applicable and more practical. The sparse reconstruction of two dimensional images is performed by making use of sampling patterns generated by taking into consideration the general frequency characteristics of natural images. In this work, instead of forming a general sampling pattern for infrared images of sea-surveillance scenarios, a special sampling pattern has been obtained by making use of a new iterative algorithm that uses a database containing images recorded under similar conditions to extract important frequency characteristics. It has been shown by experimental results that, the proposed sampling pattern provides better sparse recovery performance compared to the baseline sampling methods proposed in the literature.
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