EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution

• URL: http://arxiv.org/abs/2310.01379v1
• Date: Mon, 2 Oct 2023 17:41:56 GMT
• Title: EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution
• Authors: Esteban Reyes-Saldana and Mariano Rivera
• Abstract summary: Recent Reference-Based image super-resolution (RefSR) has improved SOTA deep methods introducing attention mechanisms to enhance low-resolution images. We propose a deep search with a more efficient memory usage that reduces significantly the number of image patches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent Reference-Based image super-resolution (RefSR) has improved SOTA deep methods introducing attention mechanisms to enhance low-resolution images by transferring high-resolution textures from a reference high-resolution image. The main idea is to search for matches between patches using LR and Reference image pair in a feature space and merge them using deep architectures. However, existing methods lack the accurate search of textures. They divide images into as many patches as possible, resulting in inefficient memory usage, and cannot manage large images. Herein, we propose a deep search with a more efficient memory usage that reduces significantly the number of image patches and finds the $k$ most relevant texture match for each low-resolution patch over the high-resolution reference patches, resulting in an accurate texture match. We enhance the Super Resolution result adding gradient density information using a simple residual architecture showing competitive metrics results: PSNR and SSMI.

Related papers

• Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution [22.60056946339325]
  We propose the Pixel Adapter Module (PAM) based on graph attention to address pixel distortion caused by upsampling. The PAM effectively captures local structural information by allowing each pixel to interact with its neighbors and update features. We demonstrate that our proposed method generates high-quality super-resolution images, surpassing existing methods in recognition accuracy.
  arXiv  Detail & Related papers  (2023-09-16T08:12:12Z)
• DBAT: Dynamic Backward Attention Transformer for Material Segmentation with Cross-Resolution Patches [8.812837829361923]
  We propose the Dynamic Backward Attention Transformer (DBAT) to aggregate cross-resolution features. Experiments show that our DBAT achieves an accuracy of 86.85%, which is the best performance among state-of-the-art real-time models. We further align features to semantic labels, performing network dissection, to infer that the proposed model can extract material-related features better than other methods.
  arXiv  Detail & Related papers  (2023-05-06T03:47:20Z)
• From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution [41.0555613285837]
  Transformer-based models have achieved competitive performances in image super-resolution (SR) We propose a new attention block whose insights are from the interpretation of Local Map (LAM) for SR networks. In the fine area, we use an Intra-Patch Self-Attention Attribution (IPSA) module to model long-range pixel dependencies in a local patch.
  arXiv  Detail & Related papers  (2022-11-30T06:32:34Z)
• {\mu}Split: efficient image decomposition for microscopy data [50.794670705085835]
  muSplit is a dedicated approach for trained image decomposition in the context of fluorescence microscopy images. We introduce lateral contextualization (LC), a novel meta-architecture that enables the memory efficient incorporation of large image-context. We apply muSplit to five decomposition tasks, one on a synthetic dataset, four others derived from real microscopy data.
  arXiv  Detail & Related papers  (2022-11-23T11:26:24Z)
• Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion [69.53542497693086]
  We evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance.
  arXiv  Detail & Related papers  (2022-10-18T11:25:19Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• HIPA: Hierarchical Patch Transformer for Single Image Super Resolution [62.7081074931892]
  This paper presents HIPA, a novel Transformer architecture that progressively recovers the high resolution image using a hierarchical patch partition. We build a cascaded model that processes an input image in multiple stages, where we start with tokens with small patch sizes and gradually merge to the full resolution. Such a hierarchical patch mechanism not only explicitly enables feature aggregation at multiple resolutions but also adaptively learns patch-aware features for different image regions.
  arXiv  Detail & Related papers  (2022-03-19T05:09:34Z)
• 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)
• Generating Superpixels for High-resolution Images with Decoupled Patch Calibration [82.21559299694555]
  Patch Networks (PCNet) is designed to efficiently and accurately implement high-resolution superpixel segmentation. DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries. In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries.
  arXiv  Detail & Related papers  (2021-08-19T10:33:05Z)
• High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network [21.977093907114217]
  Single image super-resolution is crucial for many applications such as target detection and image classification. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images.
  arXiv  Detail & Related papers  (2020-10-01T15:06:02Z)
• Texture Memory-Augmented Deep Patch-Based Image Inpainting [121.41395272974611]
  We propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions. The framework has a novel design that allows texture memory retrieval to be trained end-to-end with the deep inpainting network. The proposed method shows superior performance both qualitatively and quantitatively on three challenging image benchmarks.
  arXiv  Detail & Related papers  (2020-09-28T12:09:08Z)

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.