Study and Survey Available for Removing Fences, Reflections and Raindrops from image Pattern Recognition Techniques

Authors

  • Arvind Singh  Research Scholar,Sheat College of Engineering, Varanasi, Uttar Pradesh, India
  • Shailesh Kumar Singh  Sheat College of Engineering, Varanasi, Uttar Pradesh, India

Keywords:

Deep Convolutional Neural Network, Fence Removal, Flow Estimation, Dense Optical Flow

Abstract

The "de-fencing" process consists of two stages: the first is to identify the fence zones, and the second is to fill in the gaps. Numerous approaches to video-based de-fencing have been put out for more than ten years. However, there aren't many single-image-based approaches suggested. We concentrate on single-image fence removal in this study. Due to inadequate content information, conventional techniques have weak and incorrect fence detection and inpainting. We mix cutting-edge techniques based on a deep convolutional neural network (CNN) with conventional domain expertise in image processing to address these issues. We need to collect both the relevant non-fence ground truth photos and the fence images for the training phase. As a result, we create synthetic representations of natural fences using actual photographs. Additionally, the performance of the CNN for detection and inpainting is enhanced by spatial filtering processing (such as a Laplacian filter and a Gaussian filter). Without any human input, our suggested technology can automatically identify a fence and produce a clear photograph. Our technique works well for a variety of fence photos, according to experimental data.

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International Journal of Scientific Research in Science and Technology

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Published

2022-11-04

Issue

Volume 9, Issue 6, November-December-2022

Section

Research Articles

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How to Cite

[1]
Arvind Singh, Shailesh Kumar Singh "Study and Survey Available for Removing Fences, Reflections and Raindrops from image Pattern Recognition Techniques" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 6, pp.25-31, November-December-2022.