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A 2D image 3D reconstruction function adaptive denoising algorithm.

Feng Wang1, Weichuan Ni1, Shaojiang Liu1

  • 1Guangzhou Xinhua University, Dongguan, Guangdong, China.

Peerj. Computer Science
|October 9, 2023
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Summary
This summary is machine-generated.

This study introduces an adaptive denoising algorithm for 3D reconstruction, preserving image details often lost in traditional methods. The novel approach enhances noise immunity and 3D model fidelity for 2D images.

Keywords:
3D reconstructionAdversarial generative networkDenoising algorithmThreshold

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Area of Science:

  • Computer Vision
  • Image Processing
  • 3D Reconstruction

Background:

  • Image denoising algorithms often blur crucial details during noise reduction.
  • 3D reconstruction from 2D images faces challenges with noise and detail preservation.

Purpose of the Study:

  • To develop an adaptive denoising algorithm for 3D reconstruction of 2D images.
  • To preserve fine image details often compromised by conventional denoising methods.
  • To enhance the noise immunity and fidelity of 3D models derived from noisy 2D images.

Main Methods:

  • Image segmentation based on regional entropy values.
  • Threshold denoising for background regions.
  • Adversarial generative network processing for target regions.
  • 3D model generation from processed 2D target images.

Main Results:

  • Achieved average noise reduction exceeding 95%.
  • Successfully retained significant feature information from original images.
  • Demonstrated stable preservation of image details in experimental tests.
  • Evaluated reconstruction fidelity and noise reduction effectiveness.

Conclusions:

  • The proposed adaptive denoising algorithm effectively preserves image details during 3D reconstruction.
  • This method offers a promising solution for noise reduction challenges in 2D to 3D image conversion.
  • Enhances image quality and target information fidelity in the final 3D model.