Method for simultaneous reconstruction of a depth and clear image with a single blurred image in microscopy

The evaluation of imaging blur degradation characteristics of high-magnification optical microscopes is greatly influenced by complex imaging mechanisms, image textures, and illumination, which seriously limit the observation precision at the micro-nano scale. This paper proposes a method for simultaneous reconstruction of the depth and clear image of a blurred image based on the light intensity distribution law of the microscopic imaging system. First, based on the diffraction characteristics of the light in the circular stable cavity, the light intensity distribution function on the imaging plane of the imaging system is established, and the law of the light intensity diffusion degree with the scene depth variation is obtained by curve fitting, that is, the 3D blur degradation model of the system. Secondly, the normalized blurring degree of blurred images with different textures and different illuminations is calculated, and the mapping relationship between the blurring degree of different images and the light intensity diffusion degree of the system is established with the depth change as the intermediate variable. Thirdly, an adaptive spectral clustering method is introduced to classify the blurred images, and the weighted K-nearest neighbor method is used to automatically classify any blurred image and calculate its normalized blurring degree value and the corresponding system energy diffusion value. Based on the 3D blur degradation model and the normalized blurring degree, the depth calculation of the blurred image and the reconstruction of the clear image are realized simultaneously. The precision of the method proposed in this paper is verified by various standard nano-scale grid images and various real biological tissue samples.