Adaptive sampling schemes choose different sampling masks for different images. Blind adaptive sampling schemes use the measurements that they obtain (without any additional or direct knowledge about the image) to wisely choose the next sample mask. In this paper, we present and discuss two blind adaptive sampling schemes. The first is a general scheme not restricted to a specific class of sampling functions. It is based on an underlying statistical model for the image, which is updated according to the available measurements. A second less general but more practical method uses the wavelet decomposition of an image. It estimates the magnitude of the unsampled wavelet coefficients and samples those with larger estimated magnitude first. Experimental results show the benefits of the proposed blind sampling schemes.