A novel sampling method for the sparse recovery of infrared sea surveillance images

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 coefficients 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.