A Simple Framework for 3D Lensless Imaging with Programmable Masks

Advances in Mask-Modulated Lensless Imaging

“…The computational focusing operator can help determine the depths of the scene points, and a fast multi-depth scene reconstruction algorithm is designed that can reduce the reconstruction time by two orders of magnitude. Zheng et al [17] proposed a lensless three-dimensional imaging system that uses different patterns displayed on a programmable mask to capture the depth and intensity information of the scene. Moreover, a refinement network is devised as a postprocessing step to identify and eliminate artifacts in the reconstruction.…”

“…In the next two years, the DiffuserCam [20], PhlatCam [21], and diffractive optical element (DOE) based diffractive camera [28] were designed which further enriched phase-modulated lensless imaging. Moreover, the SweepCam [16], FAZ [12], and lensless three-dimensional imaging system [17] were designed by using amplitude-modulated masks. In 2022, the compound eye microsystem [15] and lensless polarization camera [23] were devised for amplitude and phase-modulated lensless imaging systems, accordingly.…”

“…Moreover, the paper built an experimental prototype and provided the first HS-D dataset, which was used to evaluate the performance of different deep learning algorithms on the lensless imaging system in Figure 2c. Zheng et al [17] introduced a lensless imaging strategy that can image multiple stacks in the axial direction. The paper proposed a refinement network as a postprocessing step to identify and eliminate artifacts and obtain all-in-focus images in the reconstruction.…”

Coded Illumination for 3D Lensless Imaging

“…With the small baseline between the pinholes on the mask, the coded aperture-based cameras are also able to capture the depth information of the scene [3], [5], [6], [11]- [14]. 3D reconstruction using a single snapshot of a lensless camera is an under-determined and highly ill-conditioned problem [6].…”

“…Nevertheless, deep learning-based methods in general, and end-to-end methods in particular, provide a huge variation in performance for simulated and real data (mainly because of mismatch in the simulated/actual mask-sensor-projector configuration and scenes). In contrast to deep learning methods, our method seeks to improve the conditioning of the underlying linear system and offer better generalization and robust results for arbitrary scenes without the need for learning from data [13], [14].…”