Learned lensless 3D camera

MWDNs: reconstruction in multi-scale feature spaces for lensless imaging

Lensless cameras, consisting of only a sensor and a mask, are small and flexible enough to be used in many applications with stringent scale constraints. These mask-based imagers encode scenes in caustic patterns. Most existing reconstruction algorithms rely on multiple iterations based on physical model for deconvolution followed by deep learning for perception, among which the main limitation of reconstruction quality is the mismatch between the ideal and the real model. To solve the problem, we in this work learned a class of multi Wiener deconvolution networks (MWDNs), deconvoluting in multi-scale feature spaces with Wiener filters to reduce the information loss and improving the accuracy of the given model by correcting the inputs. A comparison between the proposed and the state-of-the-art algorithms shows that ours achieves much better images and performs well in real-world environments. In addition, our method takes greater advantage of the computational time due to the abandonment of iterations.

Autofocusing of Fresnel zone aperture lensless imaging for QR code recognition

Fresnel zone aperture (FZA) lensless imaging encodes the incident light into a hologram-like pattern, so that the scene image can be numerically focused at a long imaging range by the back propagation method. However, the target distance is uncertain. The inaccurate distance causes blurs and artifacts in the reconstructed images. This brings difficulties for the target recognition applications, such as quick response code scanning. We propose an autofocusing method for FZA lensless imaging. By incorporating the image sharpness metrics into the back propagation reconstruction process, the method can acquire the desired focusing distance and reconstruct noise-free high-contrast images. By combining the Tamura of the gradient metrics and nuclear norm of gradient, the relative error of estimated object distance is only 0.95% in the experiment. The proposed reconstruction method significantly improves the mean recognition rate of QR code from 4.06% to 90.00%. It paves the way for designing intelligent integrated sensors.

Diffractive lensless imaging with optimized Voronoi-Fresnel phase

Lensless cameras are a class of imaging devices that shrink the physical dimensions to the very close vicinity of the image sensor by replacing conventional compound lenses with integrated flat optics and computational algorithms. Here we report a diffractive lensless camera with spatially-coded Voronoi-Fresnel phase to achieve superior image quality. We propose a design principle of maximizing the acquired information in optics to facilitate the computational reconstruction. By introducing an easy-to-optimize Fourier domain metric, Modulation Transfer Function volume (MTFv), which is related to the Strehl ratio, we devise an optimization framework to guide the optimization of the diffractive optical element. The resulting Voronoi-Fresnel phase features an irregular array of quasi-Centroidal Voronoi cells containing a base first-order Fresnel phase function. We demonstrate and verify the imaging performance for photography applications with a prototype Voronoi-Fresnel lensless camera on a 1.6-megapixel image sensor in various illumination conditions. Results show that the proposed design outperforms existing lensless cameras, and could benefit the development of compact imaging systems that work in extreme physical conditions.