RecDiffusion: Rectangling for Image Stitching with Diffusion Models

🌈 Abstract

The paper introduces a novel diffusion-based learning framework, RecDiffusion, for image stitching rectangling. The key points are:

• Image stitching often results in non-rectangular boundaries, which is considered unappealing. Current solutions like cropping, inpainting, or warping have limitations.
• RecDiffusion uses a two-stage approach:
  1. Motion Diffusion Models (MDM) to generate motion fields that transform stitched images with irregular edges into rectangular formats.
  2. Content Diffusion Models (CDM) to refine the images post-MDM application, especially within regions with issues.
• RecDiffusion outperforms previous traditional and deep learning-based methods on public benchmarks in both quantitative and qualitative measures.

🙋 Q&A

[01] Motion Diffusion Models (MDM)

1. What is the purpose of the Motion Diffusion Models (MDM) in the RecDiffusion framework? The purpose of the Motion Diffusion Models (MDM) is to generate motion fields that transform stitched images with irregular edges and white margins into seamlessly rectangular formats.

2. How does the MDM work? The MDM adopts an "image-to-motion" paradigm, where it takes the stitched images and their corresponding masks as input conditions, and iteratively generates motion fields that can warp the stitched images to rectangular formats.

3. What are the key components of the MDM training process? The MDM training process includes:

• Constructing "image-to-motion" diffusion models to learn the transformation from stitched images to rectangling motion fields.
• Using a loss function that combines mean square error on the motion fields and photometric loss on the warped rectangular images.

4. How does the resolution and use of stitched image masks impact the performance of MDM? The ablation study shows that:

• Higher resolution of the input images leads to better performance of MDM.
• Using the stitched image masks as conditions is crucial for MDM to outperform the baseline.

[02] Content Diffusion Models (CDM)

1. What is the purpose of the Content Diffusion Models (CDM) in the RecDiffusion framework? The purpose of the Content Diffusion Models (CDM) is to refine the images produced by the MDM, especially within regions that present issues like noise and artifacts.

2. How does the CDM work? The CDM adopts an "image-to-image" diffusion process, where it takes the stitched images and their masks as input conditions, and iteratively generates refined rectangular images.

3. What is the key strategy used in the CDM sampling process? The key strategy is a weighted sampling technique inspired by the Rank-Nullity Theorem (RNT). It preserves pixels in high-confidence regions of the MDM output, and refines the remaining regions using the CDM output.

4. How does the combination of MDM and CDM, as well as the weighted sampling mask, impact the performance? The ablation study shows that:

• Combining MDM and CDM outperforms using CDM alone.
• The weighted sampling mask further improves the performance by eliminating local distortions and restoring missing content.

[03] Comparison with Other Methods

1. How does RecDiffusion compare to previous traditional and deep learning-based rectangling methods quantitatively? RecDiffusion outperforms previous methods on the DIR-D dataset across all evaluation metrics (FID, SSIM, PSNR), establishing a new state-of-the-art.

2. How does RecDiffusion compare to previous methods qualitatively? Qualitatively, RecDiffusion is able to generate seamless rectangular images without the irregular boundaries, white edges, line discontinuities, and local distortions present in the outputs of previous warping-based methods.

3. How does RecDiffusion perform on generalization to a different dataset? RecDiffusion demonstrates strong generalization capabilities, outperforming previous methods when evaluated on the APAP-conssite dataset without any fine-tuning.

4. How does RecDiffusion compare to inpainting methods for image rectangling? Inpainting methods tend to introduce extra content that does not belong to the original images, resulting in lower PSNR/SSIM and higher FID scores compared to RecDiffusion.