Label-free microscopic cell images adaptive enhancement via weighted fusion of bright, dark, and weak structure features

Abstract

Label-free microscopic imaging techniques have been essential for cell biology research and clinical diagnosis. However, due to low contrast, weak edges, and blurry details of label-free microscopic cell images, which bring challenges for cell analysis (detecting, segmenting, and counting, etc.). Therefore, this paper proposes an image enhancement method based on the weighted fusion of bright, dark, and weak structure features to improve the visual quality and the accuracy of cell analysis of label-free microscopic cell images. Firstly, a local sliding window analysis method is used to extract the bright and dark detail features containing background and structure features, which are weighted fusion with the input image to pre-enhance the contrast and overall brightness of images. Secondly, a feature extraction method based on guided filtering and multi-scale Gaussian filtering is proposed to extract the feature information, including the edge and structure of cells, while addressing the block artifacts. And a weighted fusion of feature information is used to obtain the weak structure feature image that has multi-scale features without a background. Finally, the weighted fusion is used to fuse the detail and edge features from the guided filtered image and the weak structure feature image to improve further details and edge features of label-free microscopic cell images and obtain the enhanced image with a uniform background while structure protecting. Experiments on PNT1A, EVICAN, and Urine datasets show that the proposed method performs superior to state-of-the-art image enhancement methods in brightness improvement, edge contrast enhancement, and detail feature preservation.