Computational imaging for simple optical systems can achieve high-quality imaging by employing a joint imaging paradigm of optics and algorithms. However, when applied to remote sensing imaging, this technology faces the challenge of reconstructing large-scale, globally blurred images because of the rich information characteristics of the image. To address this issue, this paper proposes a generative adversarial network based on a multi-feature fusion Transformer residual module and a multi-scale residual network. Subsequently, using Zemax software, a real-blurred dataset is established for the optical aberrations of a remote sensing camera. Finally, a network restoration experiment is conducted. The peak signal-to-noise ratio (PSNR) of the restored image reaches 33.29 dB, with the structural similarity (SSIM) reaching 0.928, indicating that the restoration quality is close to that of an image formed by an eight-lens optical system. Additionally, in the generalization experiment, the PSNR of the restored image is 31.68 dB, and the SSIM is 0.912, with the network restoration effect being superior to those of similar studies. The experiments verify the high performance of the network proposed in this paper and effectiveness of the computational imaging model, providing a powerful tool for realizing the computational imaging model of a simple optical system for remote sensing cameras.