First-photon imaging via a hybrid penalty

• 作者: LI-JING LI MING-JIE SUN XIAO PENG XIN-YU ZHAO
• 来源期刊: 光子学研究:英文版
• 学科: 数学
• 关键词: function. PENALTY PHOTON
• 年，卷（期）: 2020,（3）
• 页码范围: 325-330
• 页数: 6页
• 分类号: O24

摘要

First-photon imaging is a photon-efficient,computational imaging technique that reconstructs an image by recording only the first-photon arrival event at each spatial location and then optimizing the recorded photon information.The optimization algorithm plays a vital role in image formation.A natural scene containing spatial correlation can be reconstructed by maximum likelihood of all spatial locations constrained with a sparsity regularization penalty,and different penalties lead to different reconstructions.The l1-norm penalty of wavelet transform reconstructs major features but blurs edges and high-frequency details of the image.The total variational penalty preserves edges better;however,it induces a"staircase effect,"which degrades image quality.In this work,we proposed a hybrid penalty to reconstruct better edge features while suppressing the staircase effect by combining wavelet l1-norm and total variation into one penalty function.Results of numerical simulations indicate that the proposed hybrid penalty reconstructed better images,which have an averaged root mean square error of 12.83%,5.68%,and 10.56%smaller than those of the images reconstructed by using only wavelet l1-norm penalty,total variation penalty,or recursive dyadic partitions method,respectively.Experimental results are in good agreement with the numerical ones,demonstrating the feasibility of the proposed hybrid penalty.Having been verified in a first-photon imaging system,the proposed hybrid penalty can be applied to other noise-removal optimization problems.