Sparsity estimation in image compressive sensing

Compressive sensing is an emerging technology which can recover a K-sparse signal vector from M = O(Klog(K=N)) measurements. However, it is a challenge to know exactly how many measurements an image requires to achieve an acceptable recovered visual quality. In this paper, we study the relationship between the image´s complexity and its sparsity. We propose a mathematical model to estimate the number of needed measurements by using the image´s texture, the edge density and the target reconstruction quality. There exists a linear function between them. The experimental results with a large number of photo pictures show that, quite most reconstructed images using our pre-calculated number of measurements have good enough quality, which confirms our proposed image-complexity-based model well.