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Related Experiment Video

Updated: Jun 2, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues
07:34

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Hallucinating face in the DCT domain.

Wei Zhang1, Wai-Kuen Cham

  • 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA. wzhang@eecs.berkeley.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 14, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new learning-based method for face hallucination, enhancing low-resolution images into high-resolution ones by focusing on frequency domain coefficients. The approach effectively reconstructs facial details for improved image quality.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Generating high-resolution (HR) images from low-resolution (LR) counterparts is a significant challenge in image processing.
  • Traditional methods often struggle with reconstructing fine facial details.

Purpose of the Study:

  • To propose a novel learning-based face hallucination framework operating in the Discrete Cosine Transform (DCT) domain.
  • To enhance the quality of face images by generating HR versions from single LR inputs.

Main Methods:

  • The problem is framed as inferring DCT coefficients in the frequency domain, rather than estimating pixel intensities in the spatial domain.
  • DC coefficients are estimated using interpolation, while a learning-based model infers AC coefficients crucial for local facial features.

Main Results:

  • The proposed method demonstrates effectiveness in producing high-quality hallucinated face images.
  • AC coefficients, vital for local features, are inferred more accurately by the learning model compared to interpolation.

Conclusions:

  • The DCT-domain approach offers a robust framework for face hallucination.
  • The learning-based inference of AC coefficients significantly improves the reconstruction of facial details.