Visual perception computing is a crucial area in artificial intelligence, aiming to simulate human vision for the intelligent analysis of complex visual data. However, current methods face several challenges, such as missing data, weak generalization across different scenarios, and difficulties in learning complex patterns, particularly in rare or long-tail situations. The framework of parallel images is reviewed in this paper, which provides new ways to advance visual perception by closely connecting real imaging systems with artificial ones. First, artificial image systems can be built to reflect real environments, enabling both real and artificial images to work together. These artificial systems produce multi-modal data, helping to solve the problem of incomplete data. Second, virtual-to-real model transfer approaches based on multi-view feature fusion are discussed, which support adaptive model improvement and better generalization to new scenarios. Finally, parallel visual models are introduced that combine data from different sources and integrate various types of knowledge, greatly improving performance on diverse visual recognition tasks.