基于分数匹配生成模型的无透镜成像方法

Lens-less imaging via score-based generative model

无透镜成像受到同轴全息图中孪生像噪声的影响,一直面临着重建信噪比差和成像分辨率低的问题。针对该问题,本文提出一种基于分数匹配生成模型的无透镜成像方法。在训练阶段,通过连续随机微分方程(Stochastic Differential Equation,SDE)缓慢添加高斯噪声扰动数据分布,然后训练具有去噪分数匹配的连续时间相关的分数函数,用于求解反向SDE生成目标样本数据。在测试阶段,使用单张菲涅尔波带片作为掩膜,在非相干光照明下实现无透镜编码调制,然后使用预测-校正的方法在数值求解器SDE和数据保真项步骤之间轮换更新进行图像重建。在LSUN-bedroom和LSUN-church数据集上的验证结果表明,提出的算法能够有效消除孪生像噪声,峰值信噪比和结构相似性分别可达25.23 dB和0.65。与传统的基于反向传播和基于压缩感知的无透镜成像结果相比,峰值信噪比分别提高17.49 dB、7.16 dB,结构相似度分别提高0.42、0.35,从而实现图像重建质量的有效提升。

Lens-less imaging is affected by twinning noise occurring in in-line holograms,and the reconstructed results continuously face poor reconstruction signal-to-noise ratio and low imaging resolution.This study proposes a lens-less imaging via a score-based generation model.In the training phase,the proposed model perturbs data distribution by gradually adding Gaussian noise by using a continuous stochastic differential equation(SDE).A continuous time-dependent score-based function with denoising score matching is then trained and used to solve the inverse SDE required to generate object sample data.In the testing phase,a single Fresnel zone aperture is used as a mask to achieve lens-less encoding modulation under incoherent illumination.The prediction-correction method is then used to alternate iteration steps between the numerical SDE solver and data-fidelity term to achieve lens-less imaging reconstruction.Validation results on LSUN-bedroom and LSUN-church datasets show that the proposed algorithm can effectively eliminate twin image noise,and the peak signal-to-noise ratio(PSNR)and structural similarity(SSIM)of the reconstruction results can reach 25.23 dB and 0.65,respectively.The PSNR values of the reconstruction results are 17.49 dB and 7.16 dB,which is higher than that of lens-less imaging algorithms based on traditional back propagation or compressed sensing,respectively.In addition,the corresponding SSIM values were 0.42 and 0.35 higher,respectively.Therefore,the reconstruction quality of the lensless imaging is effectively improved.