A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift

• URL: http://arxiv.org/abs/2411.10231v1
• Date: Fri, 15 Nov 2024 14:43:58 GMT
• Title: A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift
• Authors: Sanath Budakegowdanadoddi Nagaraju, Brian Bernhard Moser, Tobias Christian Nauen, Stanislav Frolov, Federico Raue, Andreas Dengel,
• Abstract summary: We propose TaylorIR to address limitations by utilizing a patch size of 1x1, enabling pixel-level processing in any transformer-based SR model. Experimental results demonstrate that our approach achieves new state-of-the-art SR performance while reducing memory consumption by up to 60% compared to traditional self-attention-based transformers.
• Score: 6.835244697120131
• License:
• Abstract: Transformer-based Super-Resolution (SR) models have recently advanced image reconstruction quality, yet challenges remain due to computational complexity and an over-reliance on large patch sizes, which constrain fine-grained detail enhancement. In this work, we propose TaylorIR to address these limitations by utilizing a patch size of 1x1, enabling pixel-level processing in any transformer-based SR model. To address the significant computational demands under the traditional self-attention mechanism, we employ the TaylorShift attention mechanism, a memory-efficient alternative based on Taylor series expansion, achieving full token-to-token interactions with linear complexity. Experimental results demonstrate that our approach achieves new state-of-the-art SR performance while reducing memory consumption by up to 60% compared to traditional self-attention-based transformers.

Related papers

• HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution [70.52256118833583]
  We present a strategy to convert transformer-based SR networks to hierarchical transformers (HiT-SR) Specifically, we first replace the commonly used fixed small windows with expanding hierarchical windows to aggregate features at different scales. Considering the intensive computation required for large windows, we further design a spatial-channel correlation method with linear complexity to window sizes.
  arXiv  Detail & Related papers  (2024-07-08T12:42:10Z)
• CFAT: Unleashing TriangularWindows for Image Super-resolution [5.130320840059732]
  Transformer-based models have revolutionized the field of image super-resolution (SR) We propose a non-overlapping triangular window technique that synchronously works with the rectangular one to mitigate boundary-level distortion. Our proposed model shows a significant 0.7 dB performance improvement over other state-of-the-art SR architectures.
  arXiv  Detail & Related papers  (2024-03-24T13:31:31Z)
• Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
  We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR) CFSR inherits the advantages of both convolution-based and transformer-based approaches. Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
  arXiv  Detail & Related papers  (2024-01-11T03:08:00Z)
• PTSR: Patch Translator for Image Super-Resolution [16.243363392717434]
  We propose a patch translator for image super-resolution (PTSR) to address this problem. The proposed PTSR is a transformer-based GAN network with no convolution operation. We introduce a novel patch translator module for regenerating the improved patches utilising multi-head attention.
  arXiv  Detail & Related papers  (2023-10-20T01:45:00Z)
• Vicinity Vision Transformer [53.43198716947792]
  We present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity. Our approach achieves state-of-the-art image classification accuracy with 50% fewer parameters than previous methods.
  arXiv  Detail & Related papers  (2022-06-21T17:33:53Z)
• Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution [50.10987776141901]
  Recent vision transformers along with self-attention have achieved promising results on various computer vision tasks. We introduce an effective hybrid architecture for super-resolution (SR) tasks, which leverages local features from CNNs and long-range dependencies captured by transformers. Our proposed method achieves state-of-the-art SR results on numerous benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-15T06:52:25Z)
• HRFormer: High-Resolution Transformer for Dense Prediction [99.6060997466614]
  We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks. We take advantage of the multi-resolution parallel design introduced in high-resolution convolutional networks (HRNet) We demonstrate the effectiveness of the High-Resolution Transformer on both human pose estimation and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-10-18T15:37:58Z)
• Improved Transformer for High-Resolution GANs [69.42469272015481]
  We introduce two key ingredients to Transformer to address this challenge. We show in the experiments that the proposed HiT achieves state-of-the-art FID scores of 31.87 and 2.95 on unconditional ImageNet $128 times 128$ and FFHQ $256 times 256$, respectively.
  arXiv  Detail & Related papers  (2021-06-14T17:39:49Z)

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.