Multi-scale/Multi-resolution Kronecker compressive imaging (original) (raw)

As a universal sampling procedure, compressive sensing (CS) considers that all samples of compressible signal are equally important. However, it is not true in in image/video signal since human visual system is more sensitive to low frequency component. CS theory has been extended to hybrid CS and multi-scale CS to better capture the low-frequency samples. The computational complexity, which directly proportional to spatial resolution of the target im-age/video, is another challenging CS problem which can be spatially solved by multi-resolution sensing matrix. In this paper, we propose a multi-scale/multi-resolution sensingmatrix for Kronecker CS (KCS) framework based on separable wavelet. The proposed is not only efficient sam-pling (up to 3.72dB gain) but also low complexity and compatible with convent-ional CS reconstruction. Moreover, we address measurement allocation problem with/without image sparsity prior.