Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution

• URL: http://arxiv.org/abs/2404.11273v1
• Date: Wed, 17 Apr 2024 11:25:19 GMT
• Title: Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution
• Authors: Cansu Korkmaz, A. Murat Tekalp,
• Abstract summary: Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR) To activate more input pixels globally, hybrid attention models have been proposed. We employ wavelet losses to train Transformer models to improve quantitative and subjective performance.
• Score: 6.367865391518726
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based models have achieved remarkable results in low-level vision tasks including image super-resolution (SR). However, early Transformer-based approaches that rely on self-attention within non-overlapping windows encounter challenges in acquiring global information. To activate more input pixels globally, hybrid attention models have been proposed. Moreover, training by solely minimizing pixel-wise RGB losses, such as L1, have been found inadequate for capturing essential high-frequency details. This paper presents two contributions: i) We introduce convolutional non-local sparse attention (NLSA) blocks to extend the hybrid transformer architecture in order to further enhance its receptive field. ii) We employ wavelet losses to train Transformer models to improve quantitative and subjective performance. While wavelet losses have been explored previously, showing their power in training Transformer-based SR models is novel. Our experimental results demonstrate that the proposed model provides state-of-the-art PSNR results as well as superior visual performance across various benchmark datasets.

Related papers

• Visual Fourier Prompt Tuning [63.66866445034855]
  We propose the Visual Fourier Prompt Tuning (VFPT) method as a general and effective solution for adapting large-scale transformer-based models. Our approach incorporates the Fast Fourier Transform into prompt embeddings and harmoniously considers both spatial and frequency domain information. Our results demonstrate that our approach outperforms current state-of-the-art baselines on two benchmarks.
  arXiv  Detail & Related papers  (2024-11-02T18:18:35Z)
• Effective Diffusion Transformer Architecture for Image Super-Resolution [63.254644431016345]
  We design an effective diffusion transformer for image super-resolution (DiT-SR) In practice, DiT-SR leverages an overall U-shaped architecture, and adopts a uniform isotropic design for all the transformer blocks. We analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module.
  arXiv  Detail & Related papers  (2024-09-29T07:14:16Z)
• Unveil Benign Overfitting for Transformer in Vision: Training Dynamics, Convergence, and Generalization [88.5582111768376]
  We study the optimization of a Transformer composed of a self-attention layer with softmax followed by a fully connected layer under gradient descent on a certain data distribution model. Our results establish a sharp condition that can distinguish between the small test error phase and the large test error regime, based on the signal-to-noise ratio in the data model.
  arXiv  Detail & Related papers  (2024-09-28T13:24:11Z)
• Research on Personalized Compression Algorithm for Pre-trained Models Based on Homomorphic Entropy Increase [2.6513322539118582]
  We explore the challenges and evolution of two key technologies in the current field of AI: Vision Transformer model and Large Language Model (LLM) Vision Transformer captures global information by splitting images into small pieces, but its high reference count and compute overhead limit deployment on mobile devices. LLM has revolutionized natural language processing, but it also faces huge deployment challenges.
  arXiv  Detail & Related papers  (2024-08-16T11:56:49Z)
• IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution [2.009766774844269]
  This paper extends the CFAT model to an improved GAN-based model called IG-CFAT. IG-CFAT incorporates a semantic-aware discriminator to reconstruct fine details more accurately. Our methodology adds wavelet loss to conventional loss functions of GAN-based super-resolution models to recover high-frequency details more efficiently.
  arXiv  Detail & Related papers  (2024-06-19T20:21:26Z)
• Efficient Visual State Space Model for Image Deblurring [83.57239834238035]
  Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. We propose a simple yet effective visual state space model (EVSSM) for image deblurring.
  arXiv  Detail & Related papers  (2024-05-23T09:13:36Z)
• WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal [11.757602977709517]
  This paper proposes a Transformer-based wavelet network (WaveletFormerNet) for real-world foggy image recovery. We introduce parallel convolution in the Transformer block, which allows for the capture of multi-frequency information in a lightweight mechanism. Our experiments demonstrate that our WaveletFormerNet performs better than state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-09T13:42:21Z)
• HAT: Hybrid Attention Transformer for Image Restoration [61.74223315807691]
  Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. We propose a new Hybrid Attention Transformer (HAT) to activate more input pixels for better restoration. Our HAT achieves state-of-the-art performance both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2023-09-11T05:17:55Z)
• Visformer: The Vision-friendly Transformer [105.52122194322592]
  We propose a new architecture named Visformer, which is abbreviated from the Vision-friendly Transformer' With the same computational complexity, Visformer outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy.
  arXiv  Detail & Related papers  (2021-04-26T13:13:03Z)
• Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation [6.14297871633911]
  In real-world single image super-resolution (SISR) task, the low-resolution image suffers more complicated degradations. In this paper, we focus on the camera-screen degradation and build a real-world dataset (Cam-ScreenSR) We propose a joint two-stage model. Firstly, the downsampling degradation GAN(DD-GAN) is trained to model the degradation and produces more various of LR images. Then the dual residual channel attention network (DuRCAN) learns to recover the SR image.
  arXiv  Detail & Related papers  (2020-08-01T07:10:13Z)

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.