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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This work is licensed under a Creative Commons Attribution 4.0 International License.

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.