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GP-GAN: Towards Realistic High-Resolution Image Blending

Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang

arXiv:1703.07195v3cs.CV

TL;DR

High-resolution image blending seeks realistic composites without losing detail, but classical gradient methods can produce artifacts and GANs are limited in high-frequency detail. GP-GAN combines a Blending GAN with a Gaussian-Poisson optimization, and reports state-of-the-art benchmark performance plus majority user preference. Its effectiveness is limited when composites fall far outside the training-data distribution.

  • Problem

    High-resolution image blending must make provided composite images realistic while preserving details, addressing limitations of classical methods that can produce artifacts and unrealistic outputs.

  • Method

    GP-GAN combines a Blending GAN for low-resolution color and realism with Gaussian-Poisson optimization using gradient information for high-resolution blending.

  • Results

    GP-GAN outperforms all baselines in benchmark experiments and is preferred by the majority of users in an Amazon Mechanical Turk study.

  • Takeaways & Limitations

    Combining GAN-based realism with gradient-based detail handling yields high-resolution blended images with fewer bleedings and unpleasant artifacts.

  • Takeaways & Limitations

    The algorithm fails to generate realistic images when composite images are far from the training dataset's distribution.

Abstract

from arXiv · show

It is common but challenging to address high-resolution image blending in the automatic photo editing application. In this paper, we would like to focus on solving the problem of high-resolution image blending, where the composite images are provided. We propose a framework called Gaussian-Poisson Generative Adversarial Network (GP-GAN) to leverage the strengths of the classical gradient-based approach and Generative Adversarial Networks. To the best of our knowledge, it's the first work that explores the capability of GANs in high-resolution image blending task. Concretely, we propose Gaussian-Poisson Equation to formulate the high-resolution image blending problem, which is a joint optimization constrained by the gradient and color information. Inspired by the prior works, we obtain gradient information via applying gradient filters. To generate the color information, we propose a Blending GAN to learn the mapping between the composite images and the well-blended ones. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artifacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that the majority of workers are in favor of the proposed method.

1 INTRODUCTION

GP-GAN addresses realistic high-resolution image blending by combining GAN-based realism with gradient-based detail preservation. Its two-phase framework generates a low-resolution realistic image, then applies a Gaussian-Poisson optimization across pyramid scales, outperforming baselines in benchmark and user evaluations.

  • Motivation: High-resolution blending aims to make composite images realistic while preserving their unchanged resolution, helping bridge the gap between expert and beginner editing results.Poorly blended results may cause users to abandon the editing solution after initial attempts.
  • Motivation: Classical gradient-based methods preserve high-resolution details and smooth transitions but can produce unrealistic images with artifacts because of strong prior assumptions.Poisson editing derives a gradient vector field from the composite image and reconstructs the blended image through a Poisson equation.
  • Framework: GP-GAN combines GANs and gradient-based blending methods in a two-phase framework for producing high-resolution, realistic blended images.The first phase generates a low-resolution realistic image with Blending GAN; the second solves the Gaussian-Poisson Equation using gradient and generated color information through a Laplacian pyramid.
  • Framework: Blending GAN generates low-resolution realistic images, while the Gaussian-Poisson Equation combines gradient information with color information for high-resolution blending.The framework progressively applies the optimization at finer pyramid scales after upsampling intermediate results.
  • Results: GP-GAN is reported as the first GAN-based approach for realistic high-resolution image blending and achieves state-of-the-art performance with benchmark experiments and Amazon Mechanical Turk user studies.The authors report that the method outperforms all baselines in their systematic evaluation.

2 RELATED WORK

Related work spans classical consistency-preserving and gradient-domain blending methods, GAN-based image generation, and conditional GAN applications. GP-GAN differs by using GANs as a color constraint while retaining gradient-based edge and detail handling.

  • Image Blending: Classical image blending methods seek spatial and color consistency, using pixel matching, alpha blending, or gradient-domain optimization to hide compositing artifacts.Alpha blending is fast but can blur fine details when source and target images are misregistered.
  • Image Blending: Poisson and related gradient-based methods smooth transitions and reduce foreground-background color differences, but their outputs can contain artifacts and appear unrealistic.These methods are effective for high-resolution details and textures but rely on strong assumptions about realistic image distributions.
  • Relation to Prior Work: GP-GAN differs from prior color and illumination adjustment methods by targeting smooth edge transitions and reduced color differences without requiring an accurate segmentation mask.The method can generate well-blended images with a coarse inserted-object mask.
  • Relation to Prior Work: GP-GAN introduces GAN-generated low-resolution realism as a color constraint alongside gradient-based high-resolution consistency.This design combines complementary strengths that prior approaches use separately.
  • Generative Adversarial Networks: GANs learn realistic image distributions through a zero-sum game between a generator and discriminator, while later work extends them to conditional image-to-image applications.Applications include inpainting, image manipulation, style transfer, and image-to-image translation.

3 THE APPROACH

GP-GAN blends a low-resolution realistic prediction from Blending GAN with high-resolution gradient details through a Gaussian-Poisson optimization framework. The approach combines color and gradient constraints and operates across a Laplacian pyramid to produce output at the composite image’s resolution.

  • 3.2 Framework Overview: GP-GAN applies the Gaussian-Poisson optimization across a Laplacian pyramid, progressing from the coarsest scale to the finest scale.The final output is obtained at the finest scale with the same resolution as the input composite.
  • 3.3 Blending GAN: Blending GAN generates a low-resolution realistic image that is semantically similar to the composite input.The generator is trained with supervised input and corresponding ground-truth images.
  • 3.3 Blending GAN: Blending GAN uses a combined l2 and adversarial loss, with λ set to 0.999 in the experiment.The authors motivate l2 loss as accelerating training and producing sharp, realistic images when combined with GANs.
  • 3.3 Blending GAN: The Blending GAN architecture replaces a channel-wise fully connected layer with standard fully connected layers to incorporate global image information.The authors report that convolution-only networks lacked sufficient global information for blending composite images.
  • 3.3 Blending GAN: Training targets are approximated with destination images because source and destination photos depict the same scene under different conditions.This setup provides large-scale composite images and corresponding training targets without expert-edited ground truth.
  • 3.2 Framework Overview: GP-GAN combines GAN-based image generation with gradient-based blending for high-resolution image blending.The framework is presented as a two-phase approach that uses Blending GAN and Gaussian-Poisson optimization.
  • 3.4 Gaussian-Poisson Equation: The Gaussian-Poisson objective combines a color constraint with a gradient constraint to approximate realistic colors while preserving textures and edges.The color constraint uses low-frequency signals from the generated image, while the gradient constraint restores high-resolution details from the composite image.

2 Compute ˜xl using G(x)

This step computes the low-resolution blended image ˜xl using G(x) and the upsampled source image.

  • ˜xl is computed by optimizing Equation 9 with the provided closed-form solution using xsrc and xs.The procedure then sets ˜xl to the upsampled source image.
  • The low-resolution result ˜xl is set to the upsampled xsrc image.

4 EXPERIMENTS

Experiments evaluate GP-GAN on the Transient Attributes Database using quantitative realism scores, user preferences, ablations, and visual comparisons against established blending methods.

  • Datasets: 8,571 images from 101 webcams form the Transient Attributes Database, with aligned views showing weather, time-of-day, and seasonal changes.
  • Datasets: Training uses same-camera image pairs, central-squared masks, and xdst as approximate ground truth; the trained model also handles arbitrary masks.
  • Datasets: Object-level masks annotated with LabelMe create composite images for evaluating blending methods on arbitrary masks.
  • Comparisons: GP-GAN is compared with Poisson Image Editing, Modified Poisson Image Editing, and multi-splines blending.
  • Quantitative Comparisons: GP-GAN outperforms all baselines on average realism scores evaluated by RealismCNN over 500 annotated-mask images.RealismCNN predicts realism from color, lighting, and texture compatibility.
  • User Study: In Amazon Mechanical Turk evaluation, GP-GAN is preferred by the majority of users, consistent with the realism-score results.Subjects choose the most realistic result among outputs from different algorithms.
  • Ablation: Using the Blending GAN output as the color constraint reduces bleedings and illumination inconsistencies compared with the down-sampled composite image.
  • Qualitative Comparisons: Visual comparisons report nearly no bleedings or illumination inconsistencies for GP-GAN, while baselines show bleedings and artifacts; the method also works on real high-resolution images.

5 CONCLUSION

The conclusion presents GP-GAN as an integration of GANs and gradient-based blending, while identifying a limitation on composites outside the training distribution.

  • GP-GAN combines GAN-based natural-image generation with gradient-based high-resolution local consistency to address their respective weaknesses.
  • The algorithm fails to generate realistic images when composite images are far from the training-dataset distribution.The authors identify this as future work.

A. VISUAL RESULTS

The visual-results section points readers to additional high-resolution comparisons, including results against baseline methods.

  • Additional visual results are provided in Figures 11 and 12.
  • Figures 11 and 12 compare an annotated object-level mask, composite image, MPB, MSB, and GP-GAN from top to bottom.
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