Biblio

Compressive sensing is a new technique by which sparse signals are sampled and recovered from a few measurements. To address the disadvantages of traditional space image compressing methods, a complete new compressing scheme under the compressive sensing framework was developed in this paper. Firstly, in the coding stage, a simple binary measurement matrix was constructed to obtain signal measurements. Secondly, the input image was divided into small blocks. The image blocks then would be used as training sets to get a dictionary basis for sparse representation with learning algorithm. At last, sparse reconstruction algorithm was used to recover the original input image. Experimental results show that both the compressing rate and image recovering quality of the proposed method are high. Besides, as the computation cost is very low in the sampling stage, it is suitable for on-board applications in astronomy.