编码孔径光谱成像仪在实际应用中存在着编码模板与探测器分辨率不匹配从而降低系统分辨率的问题。针对该问题进行了两种情况分析，并通过数学理论建模给出了相应的解决方案。对于编码模板分辨率高于探测器分辨率这一情况，提出引入邻域嵌入超分辨技术的方法，实现了基于压缩感知的超分辨光谱成像。对于编码模板分辨率低于探测器分辨率这一情况，提出区块阈值划分的编码孔径，将编码微元按照区块阈值重新划分并进行灰度分级，从而实现低分辨率编码模板的高分辨率编码孔径。利用梯度投影稀疏重构(GPSR)算法进行数据立方体重建，实验结果表明：运用基于超分辨理论的编码孔径快照光谱成像系统所测得的光谱图像更精准，内容更丰富；采用基于区块阈值划分的编码孔径的编码孔径快照光谱成像系统具有更高的空间分辨率和光谱分辨率。结果证实优化后的编码孔径快照光谱成像系统，其分辨率和成像质量大幅度提升，并实现了高分辨率元件的100%利用。

In practical applications, when the coding template of coded aperture spectral imagers does not match the resolution of their detector, the resolution of the system is lowered. For this problem, by using a mathematical model for the Coded Aperture Snapshot Spectral Imaging system (CASSI), its limiting factors such as a mismatch between its coding template and the detector′s resolution are analyzed and the corresponding solutions are given. Considering that the resolution of the coding template is higher than the resolution of the detector, it is proposed that super-resolution technology is introduced to the CASSI system to achieve super-resolution spectral imaging through compressed sensing. For cases in which the resolution of the coding template is lower than the resolution of the detector, a grayscale coding aperture with threshold partitioning grading is proposed to achieve a high-resolution coding mode, which can ensure the resolution of the coded aperture spectral imager. The GPSR algorithm is used to reconstruct the data cube. Experimental results show that the spectral image measured by the CASSI system based on super-resolution theory is more accurate and richer in content. The CASSI system based on a coded aperture with grayscale grading is employed and shown to have higher spatial resolution and spectral resolution. It can be concluded that after optimization, the resolution and imaging quality of the CASSI system are greatly improved and its high resolution components are fully utilized.