Abstract: Under the framework of multiscale compressed sensing in wavelet domain, the low frequency part of wavelet decomposition coefficients that contained much useful image information were completely preserved. Based on the analysis of different coefficient sub-blocks in the inter-scale and intra-scale had varied energy content, we proposed an edge-based adaptive sampling method for multiscale block compressed sensing. Firstly, we extracted edge information from low frequency coefficients, and then got the edge information value by the way of using weighted calculation on the each edge information block. Lastly, we transformed the edge information value into adaptive sample rate of each coefficient sub-blocks. The method achieved the more efficient multiscale block adaptive sampling for the compressed sensing of images. The performance of our method was verified on the three types test images: the sparse medical images, the natural images with complex texture and the SAR images with many speckle noise. The results show that it can significantly improve several compressed sensing algorithms on both the reconstructed image quality and the visual effect.