In traditional optical systems, increasing the number of lenses is usually used to improve the imaging quality of the system, but it will also lead to a substantial increase in the size, weight and cost of the optical system. To reduce the weight and volume of the optical system and expand their application range, single-element imaging has become an important direction for lightweight optical systems. Currently, single-element imaging components, such as metalenses and Fresnel zone plates (FZPs), face challenges for widespread adoption due to low diffraction efficiency, manufacturing difficulties, and performance limitations. Compared with traditional element imaging, diffractive elements offer advantages such as greater design freedom, unique dispersion characteristics, ease of fabrication, and a wider material selection.However, at the same time, the application of diffraction elements in the wide band will cause background blurring due to diffraction efficiency issues, and the unique dispersion characteristics will cause severe chromatic aberration. These are all critical issues that degrade the imaging quality. Therefore, in order to solve the above problems, this paper studies the design of optical system for larger bands, the establishment of a higher-accuracy image degradation models, the use of computational imaging technology to mitigate the degradation caused by the low efficiency diffraction of a single diffraction element, and correct the chromatic aberration. Furthermore, their impact on imaging can be compensated for through computational imaging techniques.To achieve miniaturization and lightweighting in optical systems, a design method of the CSDOE (Computational Single-layer Diffractive Optical Element) is proposed. The optimization of the CSDOE is realized by using the relationship between the thickness, the diffraction phase coefficient and the chromatic aberration of the CSDOE (Fig.1). A computational imaging method (Fig.3) is introduced to correct the chromatic aberration. The diffraction efficiency energy distribution matrix model is established by using the equivalent diffraction efficiency method, the cross-channel similarity model is established by using the similarity relationship between color channels, and the PSF (point spread function) model at the pupil is constructed by using the generalized pupil function. Combined with the above three models, a higher-accuracy image degradation function model is constructed. Through this model, an optimized iterative algorithm is established to restore the blurred color images and improve the imaging quality. And the effect of the method is verified by a design example (Fig.4). Four evaluation methods are used to compare the processed image with the blurred image.A CSDOE imaging system with a focal length of 50 mm, F value of 5 and field of view of 3° is designed, and its RMS value is 23.367 μm. Wide-band imaging was simulated using optical software (Fig.11(a)). In this paper, a high-accuracy PSF function model is constructed to mitigate the influence of diffraction efficiency and chromatic aberration, and the color image is restored (Fig.11(b)). In addition, the CSDOE imaging system was experimentally implemented (Fig.12), and captured images were restored by the model (Fig.13), which verified the feasibility of the CSDOE design method under theoretical simulation and physical tests.The CSDOE with focal length of 50 mm, F number of 5 and field of view of 3° is simulated and verified. Four evaluation methods are used to compare the processed image with the blurred image. Specifically, contrast increased by 39, gray value gradient increased by 0.5696, and standard deviation increased by 20.6064. In the actual test, contrast increased by 32, gray value gradient increased by 1.3049, and standard deviation increased by 1.2725. The contrast, gray value gradient and standard deviation of the image are increased by 17%, 22.2%, 21.7% and 17.6%, 41.1% and 10.3% respectively in simulation and actual test. In addition, in the actual test, the NIQE was reduced by 0.8902, making the image more natural overall. This paper describes an imaging system that utilizes computational imaging to eliminate the effects of chromatic aberration and diffraction efficiency on image quality, and provides new insights into the integration and miniaturization of optical systems.