Compressive sensing provides a new way for increasing the ability of information acquisition. Compressive sensing asserts that it is possible to accurately reconstruct signals from sub-Nyquist sampling, provided some additional assumptions (sparse or compressible) are made about the signal in question. The compressive imaging technology, which is based on the compressive sensing theory, integrates the processes of sensing, compression and processing perfectly, avoiding the resource waste caused by a traditional “sample-then-compress” framework. With a review of some of the recent progress in compressive sensing theory from the following three aspects: sparsity, the design of measuring matrix and recovery conditions, the reconstruction algorithms, several optical compressive imaging systems are introduced, and some key challenges in this area have been discussed in the end. 