End-to-end Wiener–CNN optimization for lensless extended depth of field imaging with phase mask

Abstract

Extended depth of field (EDoF) has been widely studied in recent years and has many applications in various imaging tasks. Currently, the EDoF imaging system is independently based on either traditional reconstruction algorithms or deep learning. Traditional algorithms, on the other hand, cannot always achieve high image quality or large depth-of-field (DoF) due to their limited model fitting ability, whereas deep learning networks always suffer from low robustness to fabrication errors. In this work, we propose a compact lensless system for EDoF that combines these two methods. First, we build an end-to-end framework based on Wiener deconvolution to optimize the imaging model. On this basis, we develop an end-to-end denoising network based on a convolutional neural network (CNN) and train it to improve the quality of the reconstructed images. The proposed system achieves superior EDoF imaging performance in image quality compared to other lensless systems. Additionally, the proposed system is demonstrated to have high robustness against sensor noise, fabrication errors and quantization errors.