Super- resolution reconstruction algorithm for optical- resolution photoacoustic microscopy images based on sparsity and deconvolution

The lateral resolution of the optical-resolution photoacoustic microscopy (OR-PAM) system depends on the focusing diameter of the probe beam. By increasing the numerical aperture (NA) of optical focusing, the lateral resolution of OR-PAM can be improved. However, the increase in NA results in smaller working distances, and the entire imaging system becomes very sensitive to small optical imperfections. The existing deconvolution-based algorithms are limited by the image signal-to-noise ratio when improving the resolution of OR-PAM images. In this paper, a super-resolution reconstruction algorithm for OR-PAM images based on sparsity and deconvolution is proposed. The OR-PAM image is sparsely reconstructed according to the constructed loss function, which utilizes the sparsity of the image to combat the decrease in the resolution. The gradient accelerated Landweber iterative algorithm is used to deconvolve to obtain high-resolution OR-PAM images. Experimental results show that the proposed algorithm can improve the resolution of mouse retinal images by approximately 1.7 times without increasing the NA of the imaging system. In addition, compared to the Richardson-Lucy algorithm, the proposed algorithm can further improve the image resolution and maintain better imaging quality, which provides a foundation for the development of OR-PAM in clinical research. (c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Automated summary

The lateral resolution of the OR-PAM system can be improved by increasing the numerical aperture of optical focusing, but this also leads to smaller working distances and increased sensitivity to optical imperfections. This paper proposes a super-resolution reconstruction algorithm based on sparsity and deconvolution for OR-PAM images, which effectively combats the decrease in resolution by utilizing the sparsity of the image and employing the gradient accelerated Landweber iterative algorithm for deconvolution. Experimental results demonstrate that the proposed algorithm can improve the resolution of mouse retinal images by approximately 1.7 times without increasing the numerical aperture of the imaging system. Additionally, it outperforms the Richardson-Lucy algorithm in terms of image resolution and quality, providing a foundation for the clinical research development of OR-PAM.