Extended depth of field for Fresnel zone aperture camera via fast passive depth estimation.

The lensless camera with incoherent illumination has gained significant research interest for its thin and flexible structure. However, it faces challenges in resolving scenes with a wide depth of field (DoF) due to its depth-dependent point spread function (PSF). In this paper, we present a single-shot method for extending the DoF in Fresnel zone aperture (FZA) cameras at visible wavelengths through passive depth estimation. The improved ternary search method is utilized to determine the depth of targets rapidly by evaluating the sharpness of the back propagation reconstruction. Based on the depth estimation results, a set of reconstructed images focused on targets at varying depths are derived from the encoded image. After that, the DoF is extended through focus stacking. The experimental results demonstrate an 8-fold increase compared with the calibrated DoF at 130 mm depth. Moreover, our depth estimation method is five times faster than the traversal method, while maintaining the same level of accuracy. The proposed method facilitates the development of lensless imaging in practical applications such as photography, microscopy, and surveillance.

Address model mismatch and defocus in FZA lensless imaging via model-driven CycleGAN.

Mask-based lensless imaging systems suffer from model mismatch and defocus. In this Letter, we propose a model-driven CycleGAN, MDGAN, to reconstruct objects within a long distance. MDGAN includes two translation cycles for objects and measurements respectively, each consisting of a forward propagation and a backward reconstruction module. The backward module resembles the Wiener-U-Net, and the forward module consists of the estimated image formation model of a Fresnel zone aperture camera (FZACam), followed by CNN to compensate for the model mismatch. By imposing cycle consistency, the backward module can adaptively match the actual depth-varying imaging process. We demonstrate that MDGAN based on either a simulated or calibrated imaging model produces a higher-quality image compared to existing methods. Thus, it can be applied to other mask-based systems.