Computational imaging is an emerging research field that combines optical systems and image processing to achieve specific imaging features. For a long time， this kind of combination in computational imaging has adopted a sequential design mode， that is， the optical system and image processing are designed separately， making it difficult to exploit the synergistic advantages of both entirely. With the rapid development of deep learning， the end-to-end co-design method of optics and image processing algorithms based on deep learning architecture has opened the door to solving this problem. On the one hand， end-to-end co-design can realise the automatic optimal collaboration of optics and image processing by comprehensively exploring the entire solution space. On the other hand， end-to-end design makes it possible to develop specific optical imaging systems. In this paper， we review recent advances in end-to-end co-design of optics and image processing， including full-colour imaging of flat lenses， large field of view imaging， extended depth of field imaging， and super-resolution imaging， and snapshot spectral imaging.