Mask-based lensless cameras replace the lens of a conventional camera with a custom mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume. Both these approaches fail to recover the scenes with large depth variations. In this paper, we propose a new approach for depth estimation based on an alternating gradient descent algorithm that jointly estimates a continuous depth map and light distribution of the unknown scene from its lensless measurements. We present simulation results on image and depth reconstruction for a variety of 3D test scenes. A comparison between the proposed algorithm and other method shows that our algorithm is more robust for natural scenes with a large range of depths. We built a prototype lensless camera and present experimental results for reconstruction of intensity and depth maps of different real objects.

中文翻译：

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使用基于掩模的无镜头相机进行联合图像和深度估计

基于遮罩的无镜头相机用定制遮罩取代了传统相机的镜头。这些相机可能非常薄，甚至很灵活。最近，已经证明这种基于掩模的相机可以恢复场景的光强度和深度信息。现有的深度恢复算法要么假设场景由少量深度平面组成，要么解决大 3D 体积上的稀疏恢复问题。这两种方法都无法恢复深度变化较大的场景。在本文中，我们提出了一种基于交替梯度下降算法的深度估计新方法，该算法通过无镜头测量联合估计未知场景的连续深度图和光分布。我们展示了各种 3D 测试场景的图像和深度重建的模拟结果。所提出的算法与其他方法的比较表明，我们的算法对于具有大深度范围的自然场景具有更强的鲁棒性。我们构建了一个原型无镜头相机，并展示了重建不同真实物体的强度和深度图的实验结果。