Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution

• URL: http://arxiv.org/abs/2207.08601v2
• Date: Thu, 21 Jul 2022 05:29:41 GMT
• Title: Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution
• Authors: Ri Cheng, Yuqi Sun, Bo Yan, Weimin Tan, Chenxi Ma
• Abstract summary: Multi-View Image Super-Resolution (MVISR) task aims to increase the resolution of multi-view images captured from the same scene. One solution is to apply image or video super-resolution (SR) methods to reconstruct HR results from the low-resolution (LR) input view. We propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view.
• Score: 16.68091352547819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent multi-view multimedia applications struggle between high-resolution (HR) visual experience and storage or bandwidth constraints. Therefore, this paper proposes a Multi-View Image Super-Resolution (MVISR) task. It aims to increase the resolution of multi-view images captured from the same scene. One solution is to apply image or video super-resolution (SR) methods to reconstruct HR results from the low-resolution (LR) input view. However, these methods cannot handle large-angle transformations between views and leverage information in all multi-view images. To address these problems, we propose the MVSRnet, which uses geometry information to extract sharp details from all LR multi-view to support the SR of the LR input view. Specifically, the proposed Geometry-Aware Reference Synthesis module in MVSRnet uses geometry information and all multi-view LR images to synthesize pixel-aligned HR reference images. Then, the proposed Dynamic High-Frequency Search network fully exploits the high-frequency textural details in reference images for SR. Extensive experiments on several benchmarks show that our method significantly improves over the state-of-the-art approaches.

Related papers

• Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow [27.31768206943397]
  Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images. Our proposed model, called EpiMISR, moves away from optical flow and explicitly uses the epipolar geometry of the acquisition process.
  arXiv  Detail & Related papers  (2024-01-30T12:55:49Z)
• Cross-View Hierarchy Network for Stereo Image Super-Resolution [14.574538513341277]
  Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views. We propose a novel method, named Cross-View-Hierarchy Network for Stereo Image Super-Resolution (CVHSSR) CVHSSR achieves the best stereo image super-resolution performance than other state-of-the-art methods while using fewer parameters.
  arXiv  Detail & Related papers  (2023-04-13T03:11:30Z)
• LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution [86.81241084950524]
  It is widely agreed that reference-based super-resolution (RefSR) achieves superior results by referring to similar high quality images, compared to single image super-resolution (SISR) Previous RefSR methods have all focused on single-reference image training, while multiple reference images are often available in testing or practical applications. We construct a large-scale, multi-reference super-resolution dataset, named LMR. It contains 112,142 groups of 300x300 training images, which is 10x of the existing largest RefSR dataset.
  arXiv  Detail & Related papers  (2023-03-09T01:07:06Z)
• Reference-based Image Super-Resolution with Deformable Attention Transformer [62.71769634254654]
  RefSR aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images. This paper proposes a deformable attention Transformer, namely DATSR, with multiple scales. Experiments demonstrate that our DATSR achieves state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-25T07:07:00Z)
• Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification [49.57112924976762]
  Cross-resolution person re-identification problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras. This paper explores an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric, which is adaptive to the resolution of a query image.
  arXiv  Detail & Related papers  (2022-07-09T03:49:51Z)
• Attention-based Multi-Reference Learning for Image Super-Resolution [29.361342747786164]
  This paper proposes a novel Attention-based Multi-Reference Super-resolution network. It learns to adaptively transfer the most similar texture from multiple reference images to the super-resolution output. It achieves significantly improved performance over state-of-the-art reference super-resolution approaches.
  arXiv  Detail & Related papers  (2021-08-31T09:12:26Z)
• Low Resolution Information Also Matters: Learning Multi-Resolution Representations for Person Re-Identification [37.01666917620271]
  Cross-resolution person re-ID aims to match person images captured from non-overlapped cameras. emphtextbfMulti-Resolution textbfRepresentations textbfJoint textbfLearning (textbfMRJL) Our method consists of a Resolution Reconstruction Network (RRN) and a Dual Feature Fusion Network (DFFN)
  arXiv  Detail & Related papers  (2021-05-26T16:54:56Z)
• Deep Burst Super-Resolution [165.90445859851448]
  We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset.
  arXiv  Detail & Related papers  (2021-01-26T18:57:21Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)
• DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution [69.2432352477966]
  Real image super-resolution(Real-SR) focus on the relationship between real-world high-resolution(HR) and low-resolution(LR) image. In this article, we propose a Dual-path Dynamic Enhancement Network(DDet) for Real-SR. Unlike conventional methods which stack up massive convolutional blocks for feature representation, we introduce a content-aware framework to study non-inherently aligned image pair.
  arXiv  Detail & Related papers  (2020-02-25T18:24:51Z)

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.