## 6 Conclusions and Discussion  
The imaging degradation of DOE in the visible spectral bandwidth caused by chromatic aberration and reduced diffraction efficiency is a long-standing problem, which limits the development and application of DOE imaging systems [36]. The application of computational imaging enables single-lens diffractive lenses to achieve high-quality imaging in the visible light bandwidth [37]. It is an essential step to analyze the image quality degradation of diffraction imaging with a wide-bandwith spectrum and to design a suitable high-performance computing imaging algorithm. In this paper, we have analyzed the image quality degradation model of HDOE imaging, and based on the analysis, we designed a DDZMR network model that performs image restoration and image super-resolution reconstruction simultaneously as the computational imaging component of SHDCIS. The SHDCIS physical prototype achieved high-quality imaging, and the ISR effect was able to improve 1.412 times. Furthermore, the DDZMR network showed excellent robustness in non-specific super-resolution tasks and denoising tasks.  
Therefore, the DDZMR network can also be applied in other imaging fields, such as microscopic imaging. In microscopic imaging, the object distance of an objective is no longer considered infinite, but rather a very short distance. Subtle objects produce an inverted, magnified image close to the focal point of the objective. Accordingly, the image degradation process for microscopic imaging is different compared to that of ordinary photographic lenses. Adjusting the loss function according to its degenerate model characteristics can make the DDZMR network get better performance. It is worth mentioning that the region division and channel planning modules that perform multi-path partitioning will play a great role in performing image restoration tasks, where the blur kernel changes greatly with position changes. This module can be generalized to more image restoration tasks, such as out-of-focus image restoration tasks, computational imaging tasks for imperfect optical systems, and deblurring tasks. With appropriate changes to the DDZMR network, it can also handle grayscale images, since there is no cross-channel specific operation for the three RGB channels involved in the DDZMR network framework.  
The subsequent research plan is to study the effect of complex environmental conditions (such as changes in temperature and air pressure) on diffraction imaging. Research on computational imaging algorithms to adapt to more complex degraded images, so that SHDCIS can still have good performance under complex environmental conditions.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12094753/s1](https://www.mdpi.com/article/10.3390/app12094753/s1)]([https://www.mdpi.com/article/10.3390/app12094753/s1](https://www.mdpi.com/article/10.3390/app12094753/s1)).  
Conceptualization, Y.X. ([PERSON]) and K.L.; methodology, Y.X. ([PERSON]); software, K.L.; validation, Y.W., N.D. and Y.Y.; investigation, H.S.; writing--original draft preparation, K.L.; writing--review and editing, X.Y.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
National Defense Science and Technology Innovation Fund of the Chinese Academy of Sciences (CXJJ-19S014); The National Key Research and Development Program of China.  
Not applicable.  
The authors declare no conflict of interest.