Computational photography is an emerging discipline that aims to combine calculations, digital technology, digital sensors, optical systems, and intelligent lighting technology, as well as hardware design and software computing abilities, to improve traditional camera imaging mechanisms.

However, due to the inherent limitations of the traditional camera mode and the resulting information, an analysis based on this has encountered bottlenecks. For example, the contradiction between the rapidly growing demand for data quality and the growing equipment cost is prominent. Furthermore, there is a large difference between ideal acquisition conditions and the actual acquisition environment, the postprocessing space of traditional means is narrow, and the inherent ambiguity of the imaging model is limited. The acquisition and processing of visual information is the main way of understanding the objective world, and it is also the foundation of computational photography for mobile intelligent systems. The application of images and videos based on deep learning have developed rapidly, whilst a series of new research directions and fields have emerged. Therefore, it is of great significance to apply deep learning to computational photography.

The aim of this Special Issue is to provide an opportunity to discuss and express views on current trends, challenges, and state-of-the-art solutions to various problems in computer photography for mobile intelligent systems. We welcome original research and review articles.

Potential topics include but are not limited to the following:

• Image super-resolution reconstruction based on deep learning
• Image enhancement based on deep learning3.Wide dynamic image enhancement
• Image stitching and synthesis
• Motion deblurring and defocus deblurring
• Image denoising based on deep learning
• Image stereo matching and 3D reconstruction
• Image restoration based on deep learning
• Light field image computing
• Image quality assessment based on deep learning