Vignetting constitutes a prevalent optical degradation that significantly compromises the quality of biomedical microscopic imaging. However, a robust and efficient vignetting correction methodology in multi-channel microscopic images remains absent at present. In this paper, we take advantage of a prior knowledge about the homogeneity of microscopic images and radial attenuation property of vignetting to develop a self-supervised deep learning algorithm that achieves complex vignetting removal in color microscopic images. Our proposed method, vignetting correction lookup table (VCLUT), is trainable on both single and multiple images, which employs adversarial learning to effectively transfer good imaging conditions from the user visually defined central region of its own light field to the entire image. To illustrate its effectiveness, we performed individual correction experiments on data from five distinct biological specimens. The results demonstrate that VCLUT exhibits enhanced performance compared to classical methods. We further examined its performance as a multi-image-based approach on a pathological dataset, revealing its advantage over other state-of-the-art approaches in both qualitative and quantitative measurements. Moreover, it uniquely possesses the capacity for generalization across various levels of vignetting intensity and an ultra-fast model computation capability, rendering it well-suited for integration into high-throughput imaging pipelines of digital microscopy.

In the traditional active contour models, global region‐based methods fail to segment images with intensity inhomogeneity, and local region‐based methods are sensitive to initial contour. In this study, a novel fuzzy energy‐based active contour model is proposed to segment medical images, which are always corrupted by intensity inhomogeneity. In order to deal with intensity inhomogeneity, a local energy term is first constructed by substituting a non‐local weight for Gaussian kernel widely used in traditional local region‐based models. Second, the defined adaptive force can drive the level set function to adaptively increase or decrease according to image intensity information. Therefore, the initial contour can be initialised as a constant function, which eliminates the problem caused by contour initialisation. Moreover, the proposed active contour model is a convex function. Thus, the problem, resulting from optimising a non‐convex functional in the traditional active contour models, can be avoided. Experimental results validate the superiorities and effectiveness of the proposed model for image segmentation with comparisons of those yielded by several state‐of‐the‐art techniques.

A growing amount of research concerning partial differential equations in image sharpening has been done for years, most of which, however, indicating edges by a binary zero-crossing decision process, produce a false result with piecewise constant regions. In this paper, a fuzzy bidirectional flow framework based on generalized fuzzy sets is proposed, which performs a fuzzy backward (inverse) diffusion along the gradient direction to the isophote lines (edges), while does a certain forward diffusion along the tangent direction on the contrary. The fuzzy membership function is controlled by the second order normal derivative of the image (or the smoothed one in the presence of noise). To preserve image features, the nonlinear diffusion coefficients are locally adjusted according to the directional derivatives of the image.

Image interpolation is a common problem in imaging applications. However, most existing interpolation algorithms in existence suffer visually the effects of blurred edges and jagged artifacts in image to some extent. This paper presents a unified bidirectional flow process, where an inverse diffusion is performed to enhance edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts ("jaggies") along the tangent directions. At the same time, adaptive processes such as soft edge decision, split-coupling scheme and feature-oriented diffusion coefficients ensure an effective preservation of image features such as edges, corners and textures. And then we apply above process to image interpolation. Experimental results demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations and relative equations.