Global deblurring for continuous out-of-focus images using a depth-varying diffusion model.

Abstract

The phenomenon of continuous out-of-focus imaging often occurs in high-magnification optical microscopy when observing large-scale targets. Lacking of accurate depth-varying point spread functions (DVPSFs) for blurred regions at different depths, it is difficult to locally reconstruct the clear images of these blurred regions using traditional deblurring methods, making it unreasonable to globally observe the optical features of large-scale targets in high-magnification optical microscopy. This paper proposes a global deblurring method for continuous out-of-focus images of large-scale sphere samples. In this study, first we analyze the energy diffusion characteristics of the optical imaging process, integrating the relationship between high-frequency energy parameters, optical range distance, and depth of field, and we propose a three-dimensional continuous energy diffusion model for optical imaging. Next, we propose an adaptive weight depth calculation method for a continuously changing surface based on the depth varying diffusion model by introducing the sample surface curvature variation and light direction. Finally, we propose a universal method for deblurring continuous out-of-focus images of large-scale sphere samples for the purpose of observing the global optical features in high-magnification optical microscopy. Moreover, we use dynamic microspheres of different sizes to verify the effectiveness of our proposed method. The results prove that our proposed method can accurately calculate the depth of the sample surface and the energy diffusion parameters at each depth, and it can achieve the image deblurring of a continuously changing surface and the global deblurring of multiple samples in a wide field of view.