Acoustic resolution photoacoustic microscopy (AR-PAM) is a novel medical imaging modality, which can be used for both structural and functional imaging in deep bio-tissue. However, the imaging resolution is degraded and structural details are lost since its dependency on acoustic focusing, which significantly constrains its scope of applications in medical and clinical scenarios. To address the above issue, model-based approaches incorporating traditional analytical prior terms have been employed, making it challenging to capture finer details of anatomical bio-structures. In this paper, we proposed an innovative prior named group sparsity prior for simultaneous reconstruction, which utilizes the non-local structural similarity between patches extracted from internal AR-PAM images. The local image details and resolution are improved while artifacts are also introduced. To mitigate the artifacts introduced by patch-based reconstruction methods, we further integrate an external image dataset as an extra information provider and consolidate the group sparsity prior with a deep denoiser prior. In this way, complementary information can be exploited to improve reconstruction results. Extensive experiments are conducted to enhance the simulated and in vivo AR-PAM imaging results. Specifically, in the simulated images, the mean peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values have increased from 16.36 dB and 0.46 to 27.62 dB and 0.92, respectively. The in vivo reconstructed results also demonstrate the proposed method achieves superior local and global perceptual qualities, the metrics of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) have significantly increased from 10.59 and 8.61 to 30.83 and 27.54, respectively. Additionally, reconstruction fidelity is validated with the optical resolution photoacoustic microscopy (OR-PAM) data as reference image.

In this paper, a fast bearing state detection method based on multi-sensor signal fusion and compression feature extraction is proposed. The best estimation in the random weighted fusion algorithm is adaptively adjusted by the fluctuation factor to realize the high-precision fusion of variable signals and reduce the noise component in the signals. In the compressed sensing framework, a partial Hadamard matrix is selected as the measurement matrix, and the signal reconstruction is abandoned, leading to reduced average sampling rate and less data for signal acquisition, transmission, and extraction of fault features. The proposed method for diagnosis of rolling bearing fault is fast, effective, and accurate, as verified by experimental results.