Open AccessLensless cameras using a mask based on almost perfect sequence through deep learning
Author(s) -
Hao Zhou,
Hao Feng,
Hu Zhen,
Zhihai Xu,
Qi Li,
Yueting Chen
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.400486
Subject(s) - computer science , artificial intelligence , image quality , iterative reconstruction , deep learning , computer vision , optics , ghost imaging , inverse problem , sequence (biology) , superresolution , property (philosophy) , autocorrelation , image (mathematics) , physics , mathematics , mathematical analysis , philosophy , statistics , epistemology , biology , genetics
Mask-based lensless imaging cameras have many applications due to their smaller volumes and lower costs. However, due to the ill-nature of the inverse problem, the reconstructed images have low resolution and poor quality. In this article, we use a mask based on almost perfect sequence which has an excellent autocorrelation property for lensless imaging and propose a Learned Analytic solution Net for image reconstruction under the framework of unrolled optimization. Our network combines a physical imaging model with deep learning to achieve high-quality image reconstruction. The experimental results indicate that our reconstructed images at a resolution of 512 × 512 have excellent performances in both visual effects and objective evaluations.