Deep learning-based filtering hyperspectral imaging technique can reconstruct hyperspectral images, which only requires deep learning and a few filters for spectral sampling. The filters are also directly integrated with the image sensor, resulting in a simple structure and quick imaging compared to typical snapshot hyperspectral imaging technology. However, most existing studies directly use the images taken by the original hyperspectral imager as the dataset without preprocessing, ignoring the impact of the original hyperspectral imager on the dataset. In this study, the dataset was preprocessed by examining the imaging mechanism of the original hyperspectral camera, which means that the hyperspectral image was converted into a radiative power spectrum to remove the effect of the original hyperspectral camera, resulting in a more robust model than in previous studies. Furthermore, because the spectral response function has poor smoothness, the filters are difficult to produce; thus, the smoothness constraint is incorporated into the error function to create a smooth and easy-to-produce filter.