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Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
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An improved pix2pix model based on Gabor filter for robust color image rendering.

Hong-An Li1, Min Zhang1, Zhenhua Yu1

  • 1College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710054, China.

Mathematical Biosciences and Engineering : MBE
|December 14, 2021
PubMed
Summary

This study introduces an improved deep learning method for robust image color rendering. The Gabor filter and enhanced pix2pix model overcome detail loss and training instability, yielding better visual quality.

Keywords:
gabor filtergenerative adversarial networksimage renderingleast squares loss functionpenalty termpix2pix model

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Processing

Background:

  • Deep learning has revitalized research in image color rendering.
  • Existing methods struggle with color overstepping, boundary blurring, and unstable generative adversarial network (GAN) training.

Purpose of the Study:

  • To propose a robust image color rendering method using a Gabor filter-based improved pix2pix model.
  • To address detail loss and training instability issues in current color rendering techniques.

Main Methods:

  • Utilized Gabor filters for multi-direction/multi-scale image preprocessing to preserve detailed features.
  • Selected Gabor texture maps at scale 7 and 0° direction for optimal rendering.
  • Improved the pix2pix model's loss function with a penalty term for stable training.

Main Results:

  • The proposed method achieves comparable rendering performance using specific Gabor filter parameters.
  • The enhanced pix2pix model demonstrated stabilized training and produced ideal color images.
  • Quantitative evaluation using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) confirmed improved image quality.

Conclusions:

  • The Gabor filter-based improved pix2pix method offers superior visual performance for robust image color rendering.
  • The technique effectively reduces the impact of light and noise on rendered images.