论文标题
无透镜相机的展开的原始二重网络
Unrolled Primal-Dual Networks for Lensless Cameras
论文作者
论文摘要
无透镜摄像机的常规图像重建模型通常假定每个测量值是通过将给定场景与单个实验测量的点传播函数相连引起的。这些图像重建模型在真实地模拟无透镜相机的过程中缺乏，因为这些模型还不够复杂，无法说明具有深度变化的光学畸变或场景。我们的工作表明，学习一种监督的原始双重重建方法会导致文献中的图像质量匹配状态，而无需大量的网络容量。这一改进源于我们的主要发现，即在学习的原始偶尔二优化框架中嵌入可学习的前进和伴随模型甚至可以改善重建图像（+5db psnr）的质量，而不是正确的模型误差的作品。此外，我们构建了概念验证镜头相机原型，该原型使用伪随机相掩码来展示我们的观点。最后，我们根据开放数据集和数据集分享了对我们学到的模型的广泛评估，从我们的概念证明无镜头相机原型。
Conventional image reconstruction models for lensless cameras often assume that each measurement results from convolving a given scene with a single experimentally measured point-spread function. These image reconstruction models fall short in simulating lensless cameras truthfully as these models are not sophisticated enough to account for optical aberrations or scenes with depth variations. Our work shows that learning a supervised primal-dual reconstruction method results in image quality matching state of the art in the literature without demanding a large network capacity. This improvement stems from our primary finding that embedding learnable forward and adjoint models in a learned primal-dual optimization framework can even improve the quality of reconstructed images (+5dB PSNR) compared to works that do not correct for the model error. In addition, we built a proof-of-concept lensless camera prototype that uses a pseudo-random phase mask to demonstrate our point. Finally, we share the extensive evaluation of our learned model based on an open dataset and a dataset from our proof-of-concept lensless camera prototype.
