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DVDR-SRGAN: Differential Value Dense Residual Super-Resolution Generative Adversarial Network.

Hang Qu1, Huawei Yi1, Yanlan Shi1

  • 1School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
Summary

This study introduces a novel differential value dense residual network (DVDR-SRGAN) for single-image super-resolution. The model enhances image reconstruction by focusing on critical areas, improving visual quality and detail accuracy.

Keywords:
differential value dense residual networkgenerative adversarial networkimage reconstructionjoint loss functionsuper resolution

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Area of Science:

  • Computer Vision
  • Image Processing
  • Deep Learning

Background:

  • Generative Adversarial Networks (GANs) excel at generating human-eye-pleasing image textures in super-resolution.
  • However, GANs often introduce artifacts, false textures, and detail discrepancies compared to ground truth images.
  • Existing methods struggle to fully capture and reconstruct fine image details accurately.

Purpose of the Study:

  • To improve visual quality in single-image super-resolution reconstruction.
  • To address artifacts and detail deviations common in GAN-based super-resolution.
  • To enhance feature correlation analysis between adjacent network layers.

Main Methods:

  • Proposed a differential value dense residual network (DVDR-SRGAN) focusing on feature correlation.
  • Employed deconvolution for feature enlargement, followed by convolution for feature extraction.
  • Calculated differential values between pre- and post-extraction features to highlight areas needing attention.
  • Utilized dense residual connections for complete feature magnification and accurate differential value extraction.
  • Introduced a joint loss function to fuse high-frequency and low-frequency information for improved visual effects.

Main Results:

  • The DVDR-SRGAN model demonstrated improved performance on standard datasets (Set5, Set14, BSD100, Urban).
  • Achieved superior results in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) metrics.
  • Outperformed established models including Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR.

Conclusions:

  • The proposed DVDR-SRGAN effectively mitigates artifacts and enhances detail reconstruction in single-image super-resolution.
  • Feature correlation analysis and differential value extraction are crucial for improving super-resolution accuracy.
  • The joint loss function further refines visual quality by integrating multi-frequency information.