Mathematical Modeling and Implementation of Multi-Scale Attention Feature Enhancement Network Algorithm for the Clarity of SEM and TEM Images
Keywords:
Image Dehazing, Image restoration, Feature Enhancement, Fusion, Progressive FeatureAbstract
In this paper, the approach aims to improve image clarity and visual effects by considering both high and low-frequency haze characteristics. The inclusion of guidance information in cloudy appearances and the success of benchmark datasets, as well as real-world scanning electron microscope (SEM) and transmission electron microscope (TEM) images, suggest the versatility and practical applicability of the algorithm across different domains. The algorithm begins by utilizing a neural network trained to establish a mapping between hazy images and their corresponding clear versions. A progressive feature fusion module is introduced to enhance the utilization of guidance information from the generated reference image. This module combines features extracted from the hazy and reference pictures. The use of progressive feature fusion is highlighted, indicating a sophisticated approach to combining information from different sources This could help in preserving important details and structures during the dehazing process. Validation on Benchmark Datasets and Real-world Images: The practical applicability of the algorithm is demonstrated through its success on benchmark datasets, providing a standard for comparison, and real-world SEM and TEM images, showcasing its versatility across various domains. The combination of deep learning, progressive feature fusion, and end-to-end training is a robust framework for effective image dehazing, as demonstrated by the algorithm's impressive results in controlled datasets and real-world scenarios.
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