Improving the Harmony of the Composite Image by Spatial-Separated Attention Module (1907.06406v3)

Abstract: Image composition is one of the most important applications in image processing. However, the inharmonious appearance between the spliced region and background degrade the quality of the image. Thus, we address the problem of Image Harmonization: Given a spliced image and the mask of the spliced region, we try to harmonize the "style" of the pasted region with the background (non-spliced region). Previous approaches have been focusing on learning directly by the neural network. In this work, we start from an empirical observation: the differences can only be found in the spliced region between the spliced image and the harmonized result while they share the same semantic information and the appearance in the non-spliced region. Thus, in order to learn the feature map in the masked region and the others individually, we propose a novel attention module named Spatial-Separated Attention Module (S2AM). Furthermore, we design a novel image harmonization framework by inserting the S2AM in the coarser low-level features of the Unet structure in two different ways. Besides image harmonization, we make a big step for harmonizing the composite image without the specific mask under previous observation. The experiments show that the proposed S2AM performs better than other state-of-the-art attention modules in our task. Moreover, we demonstrate the advantages of our model against other state-of-the-art image harmonization methods via criteria from multiple points of view. Code is available at https://github.com/vinthony/s2am

• The paper introduces S²AM, a novel module that enhances image harmonization by distinctly processing spliced and non-spliced image regions.
• The methodology integrates individual attention mechanisms within a U-net, replacing conventional skip connections with self-generated masks.
• Experimental results demonstrate significant improvements in metrics like MSE, SSIM, and PSNR on both synthesized datasets and real-world evaluations.

Assessment of "Improving the Harmony of the Composite Image by Spatial-Separated Attention Module"

The paper "Improving the Harmony of the Composite Image by Spatial-Separated Attention Module" introduces a novel approach to address the persistent issue of image harmonization. Image harmonization involves making the spliced region of a composite image consistent in appearance with the background, thus enhancing image realism. The authors propose a Spatial-Separated Attention Module (S$^{2}$AM) which improves the separation and individual learning of features between spliced and non-spliced regions during the image harmonization process.

The paper argues that existing methods inadequately address image harmonization by primarily relying on neural network learning without sufficiently distinguishing between altered and unchanged image areas. S$^{2}$AM is designed to simultaneously analyze spliced and non-spliced regions using individual attention mechanisms, enhancing the neural network’s ability to learn divergences in spliced regions and maintain consistency elsewhere.

Highlights and Methodology

The key observation driving this research is that harmonization requires a specialized approach to low-level feature differences in the spliced region and consistency in high-level semantic features across the entire image. The S$^{2}$AM system is embedded within a U-net structure, effectively replacing traditional skip connections. Notably, the paper further eliminates the need for a predefined mask by employing a self-generated mask using spatial attention in combination with attention loss.

• S$^{2}$AM Details:
  • The module comprises three channel attention gates to manage different feature aspects: $G_{fg}$ (foreground differences), $G_{mix}$ (unchanged details in the spliced area), and $G_{bg}$ (background consistencies).
  • A significant innovation is the use of hard-coded Gaussian-smoothed masks, enhancing boundary harmonization without explicit supervision.
  • The framework employs attention loss to align generated spatial attention maps with true masks, enabling effective mask prediction.

Experimental Evaluation

The authors provide a comprehensive evaluation on synthesized datasets derived from COCO and Adobe5K, demonstrating the proposed method’s effectiveness. The experiments span numerical assessments using MSE, SSIM, and PSNR metrics, as well as realism prediction with pre-trained CNNs for perceptual evaluation. Results showcase significant improvements over existing methods such as Deep Image Harmonization and RealismCNN.

• Performance:
  • Consistent improvements are observed across numerical and perceptual metrics in both synthesized datasets.
  • Ultimately, the extension to real-world scenarios, illustrated through a user paper, confirms the module’s robustness and applicability beyond controlled datasets.

Implications and Future Directions

The development of S$^{2}$AM introduces notable theoretical and practical enhancements to image harmonization tasks. By differentiating feature processing based on targeted regions, the system sets a precedent for other computer vision problems requiring focused regional adjustments, such as image inpainting and semantic segmentation.

This work paves the way for future research to delve into unsupervised methods for identifying spliced regions dynamically, promoting further automation in digital image processing tasks. Moreover, the integration of more complex attention networks and fine-tuned loss functions may refine harmonization even further. It encourages a broader exploration of attention mechanisms within pixel-level transformations, pushing the boundaries of current applications in AI-driven image synthesis and editing.

Overall, the paper delivers a substantial contribution to the field, providing a robust framework connecting attentive feature learning with practical image harmonization, meriting further investigation and development in AI applications for digital content creation.