Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning

• URL: http://arxiv.org/abs/2010.01810v2
• Date: Mon, 9 Nov 2020 05:18:19 GMT
• Title: Painting Outside as Inside: Edge Guided Image Outpainting via Bidirectional Rearrangement with Progressive Step Learning
• Authors: Kyunghun Kim, Yeohun Yun, Keon-Woo Kang, Kyeongbo Kong, Siyeong Lee, Suk-Ju Kang
• Abstract summary: We propose a novel image outpainting method using bidirectional boundary region rearrangement. The proposed method is compared with other state-of-the-art outpainting and inpainting methods both qualitatively and quantitatively. The experimental results demonstrate that our method outperforms other methods and generates new images with 360degpanoramic characteristics.
• Score: 18.38266676724225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image outpainting is a very intriguing problem as the outside of a given image can be continuously filled by considering as the context of the image. This task has two main challenges. The first is to maintain the spatial consistency in contents of generated regions and the original input. The second is to generate a high-quality large image with a small amount of adjacent information. Conventional image outpainting methods generate inconsistent, blurry, and repeated pixels. To alleviate the difficulty of an outpainting problem, we propose a novel image outpainting method using bidirectional boundary region rearrangement. We rearrange the image to benefit from the image inpainting task by reflecting more directional information. The bidirectional boundary region rearrangement enables the generation of the missing region using bidirectional information similar to that of the image inpainting task, thereby generating the higher quality than the conventional methods using unidirectional information. Moreover, we use the edge map generator that considers images as original input with structural information and hallucinates the edges of unknown regions to generate the image. Our proposed method is compared with other state-of-the-art outpainting and inpainting methods both qualitatively and quantitatively. We further compared and evaluated them using BRISQUE, one of the No-Reference image quality assessment (IQA) metrics, to evaluate the naturalness of the output. The experimental results demonstrate that our method outperforms other methods and generates new images with 360{\deg}panoramic characteristics.

Related papers

• Sketch-guided Image Inpainting with Partial Discrete Diffusion Process [5.005162730122933]
  We introduce a novel partial discrete diffusion process (PDDP) for sketch-guided inpainting. PDDP corrupts the masked regions of the image and reconstructs these masked regions conditioned on hand-drawn sketches. The proposed novel transformer module accepts two inputs -- the image containing the masked region to be inpainted and the query sketch to model the reverse diffusion process.
  arXiv  Detail & Related papers  (2024-04-18T07:07:38Z)
• Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach [104.2588068730834]
  This paper pushes the technical frontier of image outpainting in two directions that have not been resolved in literature. We develop a method that does not depend on a pre-trained backbone network. We evaluate the proposed approach (called PQDiff) on public benchmarks, demonstrating its superior performance over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-01-28T13:00:38Z)
• Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond [136.18504104345453]
  We present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting. The proposed algorithm can be effectively extended to other panoramic vision tasks, such as object detection, depth estimation, and image super-resolution.
  arXiv  Detail & Related papers  (2022-04-18T21:18:49Z)
• In&Out : Diverse Image Outpainting via GAN Inversion [89.84841983778672]
  Image outpainting seeks for a semantically consistent extension of the input image beyond its available content. In this work, we formulate the problem from the perspective of inverting generative adversarial networks. Our generator renders micro-patches conditioned on their joint latent code as well as their individual positions in the image.
  arXiv  Detail & Related papers  (2021-04-01T17:59:10Z)
• Free-Form Image Inpainting via Contrastive Attention Network [64.05544199212831]
  In image inpainting tasks, masks with any shapes can appear anywhere in images which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. We propose a self-supervised Siamese inference network to improve the robustness and generalization.
  arXiv  Detail & Related papers  (2020-10-29T14:46:05Z)
• Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes [54.836331922449666]
  We propose a Semantic Guidance and Evaluation Network (SGE-Net) to update the structural priors and the inpainted image. It utilizes semantic segmentation map as guidance in each scale of inpainting, under which location-dependent inferences are re-evaluated. Experiments on real-world images of mixed scenes demonstrated the superiority of our proposed method over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-03-15T17:49:20Z)
• Image Inpainting by Multiscale Spline Interpolation [9.561123408923489]
  We propose a multi-scale image inpainting method that utilizes both local and global features. On average, we achieved 1.2 dB improvement over some existing inpainting approaches.
  arXiv  Detail & Related papers  (2020-01-10T01:15:14Z)
• Very Long Natural Scenery Image Prediction by Outpainting [96.8509015981031]
  Outpainting receives less attention due to two challenges in it. First challenge is how to keep the spatial and content consistency between generated images and original input. Second challenge is how to maintain high quality in generated results.
  arXiv  Detail & Related papers  (2019-12-29T16:29:01Z)

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.