在光声层析成像（photoacoustic tomography，PAT）时，不均匀光通量分布、组织复杂的光学和声学特性以及超声探测器的非理想特性等因素会导致重建图像质量下降。本文考虑不均匀光通量、非定常声速、超声探测器的空间脉冲响应和电脉冲响应、有限角度扫描和稀疏采样等因素的影响，建立了前向成像模型。通过交替优化求解成像模型的逆问题，实现光吸收能量分布图和声速分布图的同时重建。仿真、仿体和在体实验结果表明，与反投影法、时间反演法和短滞后空间相干法相比，该方法重建图像的结构相似度和峰值信噪比可分别提高约83%、56%、22%和80%、68%、58%。由上述结果可知，对非理想成像场景采用该方法重建的图像质量有显著提高。

Sparse coding is a prevalent method for image inpainting and feature extraction, which can repair corrupted images or improve data processing efficiency, and has numerous applications in computer vision and signal processing. Recently, several memristor-based in-memory computing systems have been proposed to enhance the efficiency of sparse coding remarkably. However, the variations and low precision of the devices will deteriorate the dictionary, causing inevitable degradation in the accuracy and reliability of the application. In this work, a digital-analog hybrid memristive sparse coding system is proposed utilizing a multilevel Pt/Al₂O₃/AlO_x/W memristor, which employs the forward stagewise regression algorithm: The approximate cosine distance calculation is conducted in the analog part to speed up the computation, followed by high-precision coefficient updates performed in the digital portion. We determine that four states of the aforementioned memristor are sufficient for the processing of natural images. Furthermore, through dynamic adjustment of the mapping ratio, the precision requirement for the digit-to-analog converters can be reduced to 4 bits. Compared to the previous system, our system achieves higher image reconstruction quality of the 38 dB peak-signal-to-noise ratio. Moreover, in the context of image inpainting, images containing 50% missing pixels can be restored with a reconstruction error of 0.0424 root-mean-squared error.