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Robust Nonparametric Distribution Transfer with Exposure Correction for Image Neural Style Transfer.

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This study introduces two novel image neural style transfer models that address color differences and detail loss. These methods improve stylized image quality, especially for underexposed content images.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Image neural style transfer (NST) uses convolutional neural networks (CNNs) to apply the style of one image to the content of another.
  • Existing NST models struggle with color discrepancies and preserving content details, limiting their practical application.
  • Underexposed images present additional challenges for NST, often resulting in degraded stylized outputs.

Purpose of the Study:

  • To propose two advanced NST models addressing limitations of current methods.
  • To enhance the quality of stylized images, particularly concerning color fidelity and detail preservation.
  • To specifically improve NST for underexposed content images.

Main Methods:

  • Developed two NST models leveraging robust nonparametric distribution transfer.
  • Implemented a color probability density function transfer to align content and style image colors.
  • Introduced an adaptive detail-enhanced exposure correction algorithm for underexposed images.

Main Results:

  • The first model improves spatial structure, especially when content image color dynamic range is smaller than the style image.
  • The second model significantly enhances stylized results for underexposed content images.
  • Both proposed models demonstrate superior qualitative and quantitative performance compared to popular existing methods.

Conclusions:

  • The proposed nonparametric distribution transfer models offer significant improvements in image neural style transfer.
  • These methods effectively address color differences and enhance detail preservation in stylized images.
  • The adaptive exposure correction model provides a robust solution for NST on underexposed images.