Holographic storage technology， as a kind of data storage technology with three-dimensional volume storage and two-dimensional data transmission， is characterized by high storage density and fast data transmission， which is one of the powerful solutions for long-term storage of massive data. The traditional holographic storage method is limited by the fact that the photodetector only responds to intensity， and is usually modulated by pure amplitude coding. However， utilizing only amplitude information cannot fully exploit the advantages of holography itself， and how to decode the phase information in a simple， fast， stable and accurate way is a real problem faced by holographic storage technology. Computational imaging opens a new way to solve phase retrieval problem for holographic storage technology because of its algorithmic versatility， high perceptual dimension characteristics and so on. This paper mainly reviews some work in recent years on solving the phase retrieval problem of holographic storage using computational imaging technology from the perspectives of iterative computational phase retrieval and deep learning phase retrieval. Analyses are conducted on the work from the perspectives of improving storage density， data reading speed， and data reading stability. Finally， we make an outlook on the future development of this direction.