Abstract: A method for adaptive reconstruction of a compressively sensed data. The method contains the steps of receiving sensed data; conducting an initial reconstruction to the sensed data to obtain a plurality of first reconstruction patches; by a reconstruction module, conducting a progressive reconstruction to the sensed data to obtain a plurality of second reconstruction patches; summing the plurality of second reconstruction patches with the a plurality of first reconstruction patches to obtain final patches; and merging the final patches to obtain a reconstructed data. The progressive reconstruction further contains concatenating transformer features and convolution features to obtain the second reconstruction patches. The invention provides a hybrid network for adaptive sampling and reconstruction of CS, which integrates the advantages of leveraging both detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning.