A novel cell image fusion approach based on the collaboration of multilevel latent Low-Rank representation and the convolutional neural network

Abstract

Multilevel cell image fusion is an image enhancement technique that improves the quality of images. The procedure of the multilevel approach follows an image decomposition and fusion strategy. The image decomposition phase partitions the medical observations into the basic (luminance and contour information) and detailed (structural information) components. Whereas the image fusion stage reconstructs the fused image from the decomposed data patterns. Recent techniques have paid more attention to the fusion of detailed components. These components contain hidden data patterns and they are fused with a well-adopted strategy. The basic components are considered less important features and they are combined with weighted sum rules. Which results in the generation of an insignificant feature map containing more detailed texture and less luminance information. We have addressed the given problem by performing the efficient fusion of sub-components. The basic components of the image are affected by blur regions and varying illuminations. These distortions are minimized by the CNN-based fusion approach. Which generates a powerful decision map by processing the features through multiple stages, which include focus map generation, binary segmentation, consistency verification, and decision map. Parallel strategies are followed for the fusion of detailed data components. These components are combined with a nuclear-norm-based fusion framework. Which is the efficient fusion policy for the combination of local structural patterns. We have evaluated the model by nine quality metrics. The performance of the proposed model is compared with state-of-art methods. It has overpowered the related techniques in qualitative and quantitative analysis.