Mask-based lensless imaging cameras have many applications due to their smaller volumes and lower costs. However, due to the ill-nature of the inverse problem, the reconstructed images have low resolution and poor quality. In this article, we use a mask based on almost perfect sequence which has an excellent autocorrelation property for lensless imaging and propose a Learned Analytic solution Net for image reconstruction under the framework of unrolled optimization. Our network combines a physical imaging model with deep learning to achieve high-quality image reconstruction. The experimental results indicate that our reconstructed images at a resolution of 512 × 512 have excellent performances in both visual effects and objective evaluations.

中文翻译：

---

无镜头相机使用基于深度学习的几乎完美序列的遮罩

基于掩模的无镜头成像相机由于其体积较小和成本较低而具有许多应用。但是，由于反问题的性质，重建的图像分辨率低，质量差。在本文中，我们使用了基于几乎完美序列的遮罩，该遮罩对无透镜成像具有出色的自相关特性，并提出了在展开优化框架下用于图像重建的Learned Analytic Solution Net。我们的网络将物理成像模型与深度学习相结合，以实现高质量的图像重建。实验结果表明，我们以512×512的分辨率重建的图像在视觉效果和客观评估方面均具有出色的表现。