STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Polymer & Composites Review Article Special issue

A Hybrid model of ResNet50 integrated U-Net for image Denoising for Polymer and Composite Microstructure Analysis

  1. Akanksha Kochhar Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering
  2. Rupali Pandey Department of Applied Sciences, Bharati Vidyapeeth’s College of Engineering
  3. Aarti Sehwag Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering
  4. Sanskar Kannaujia Department of Electronics and Communication Engineering, Bharati Vidyapeeth’s College of Engineering
  5. Anu Yadav* Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering

Abstract

In digital era a high-quality imaging plays a very important role in polymer and composite material characterization features such as fiber-matrix interfaces, voids, microcracks cause problems in mechanical and functional properties. Polymer imaging includes optical microscopy and scanning electron microscopy, due to sensor limitation, environmental conditions add noise to the image and reduce quality of image. The noise degrades image quality, and it leads to reduce reliability in material analysis. So, there is need to reduce noise while preserving image features are desperately needed. To solve this problem, a hybrid model is introducing that uses deep learning models for noise reduction. Proposed method improves feature extraction by using skip connections and an attention mechanism, which prioritizes important information that is important for noise reduction. The method utilizes a diverse multimodal data set to train a robust noise reduction model based on the refined U-Net architecture, making it suitable for noise-prone imaging scenarios encountered in polymer science and materials engineering. Performance further enhanced with transfer learning from ResNet50. This approach successfully reduces noise. The proposed method shows strong capabilities to handle multi noise type and data quality across diverse applications, including polymer microstructure analysis, defect detection, and material quality assessment.

Keywords

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