Assessing Image Inpainting via Re-Inpainting Self-Consistency Evaluation

• URL: http://arxiv.org/abs/2405.16263v1
• Date: Sat, 25 May 2024 15:05:08 GMT
• Title: Assessing Image Inpainting via Re-Inpainting Self-Consistency Evaluation
• Authors: Tianyi Chen, Jianfu Zhang, Yan Hong, Yiyi Zhang, Liqing Zhang,
• Abstract summary: We introduce an innovative evaluation paradigm that utilizes a self-supervised metric based on multiple re-inpainting passes. This approach emphasizes the principle of self-consistency to enable the exploration of various viable inpainting solutions.
• Score: 46.974439781041774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image inpainting, the task of reconstructing missing segments in corrupted images using available data, faces challenges in ensuring consistency and fidelity, especially under information-scarce conditions. Traditional evaluation methods, heavily dependent on the existence of unmasked reference images, inherently favor certain inpainting outcomes, introducing biases. Addressing this issue, we introduce an innovative evaluation paradigm that utilizes a self-supervised metric based on multiple re-inpainting passes. This approach, diverging from conventional reliance on direct comparisons in pixel or feature space with original images, emphasizes the principle of self-consistency to enable the exploration of various viable inpainting solutions, effectively reducing biases. Our extensive experiments across numerous benchmarks validate the alignment of our evaluation method with human judgment.

Related papers

• Multi-target stain normalization for histology slides [6.820595748010971]
  We introduce a novel approach that leverages multiple reference images to enhance robustness against stain variation. Our method is parameter-free and can be adopted in existing computational pathology pipelines with no significant changes.
  arXiv  Detail & Related papers  (2024-06-04T07:57:34Z)
• Learning from Multi-Perception Features for Real-Word Image Super-resolution [87.71135803794519]
  We propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information. We also introduce a contrastive regularization term (CR) that improves the model's learning capability.
  arXiv  Detail & Related papers  (2023-05-26T07:35:49Z)
• CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition [3.1677775852317085]
  We propose Contrastive Regularization for Face recognition (CoReFace) to apply image-level regularization in feature representation learning. Specifically, we employ sample-guided contrastive learning to regularize the training with the image-image relationship directly. To integrate contrastive learning into face recognition, we augment embeddings instead of images to avoid the image quality degradation.
  arXiv  Detail & Related papers  (2023-04-23T14:33:24Z)
• Detecting Recolored Image by Spatial Correlation [60.08643417333974]
  Image recoloring is an emerging editing technique that can manipulate the color values of an image to give it a new style. In this paper, we explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring. Our method achieves the state-of-the-art detection accuracy on multiple benchmark datasets and exhibits well generalization for unknown types of recoloring methods.
  arXiv  Detail & Related papers  (2022-04-23T01:54:06Z)
• Fast Hybrid Image Retargeting [0.0]
  We propose a method that quantifies and limits warping distortions with the use of content-aware cropping. Our method outperforms recent approaches, while running in a fraction of their execution time.
  arXiv  Detail & Related papers  (2022-03-25T11:46:06Z)
• Deblurring via Stochastic Refinement [85.42730934561101]
  We present an alternative framework for blind deblurring based on conditional diffusion models. Our method is competitive in terms of distortion metrics such as PSNR.
  arXiv  Detail & Related papers  (2021-12-05T04:36:09Z)
• Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment [157.1292674649519]
  We propose a practical solution named degraded-reference IQA (DR-IQA) DR-IQA exploits the inputs of IR models, degraded images, as references. Our results can even be close to the performance of full-reference settings.
  arXiv  Detail & Related papers  (2021-08-18T02:35:08Z)
• Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction [5.129343375966527]
  We propose a scalable and efficient framework to simultaneously quantify aleatoric and epistemic uncertainties in learned iterative image reconstruction. We show that our method exhibits competitive performance against conventional benchmarks for computed tomography with both sparse view and limited angle data.
  arXiv  Detail & Related papers  (2020-11-17T04:12:52Z)
• Structured and Localized Image Restoration [141.75042935077465]
  We present a novel approach to image restoration that leverages ideas from localized structured prediction and non-linear multi-task learning. We derive the corresponding algorithms for energies based on the mean-squared and Euclidean norm errors.
  arXiv  Detail & Related papers  (2020-06-16T15:43:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.