Database based fourier coefficient sampling for sparse recovery of infrared sea surface platform images

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 any image. In this work, instead of forming a general sampling pattern for infrared images of sea surface platforms, a special sampling pattern has been obtained by making use of a database containing images recorded under similar atmospheric conditions. It has been shown by experimental results that the proposed sampling pattern provides better sparse recovery compared to the widely used pattern proposed in the literature.