DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer

• URL: http://arxiv.org/abs/2207.13861v1
• Date: Thu, 28 Jul 2022 02:33:57 GMT
• Title: DnSwin: Toward Real-World Denoising via Continuous Wavelet Sliding-Transformer
• Authors: Hao Li, Zhijing Yang, Xiaobin Hong, Ziying Zhao, Junyang Chen, Yukai Shi, Jinshan Pan
• Abstract summary: We propose a continuous Wavelet Sliding-Transformer that builds frequency correspondence under real-world scenes. Specifically, we first extract the bottom features from noisy input images by using a CNN encoder. We reconstruct the deep features into denoised images using a CNN decoder.
• Score: 40.21145302686399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image denoising is a practical image restoration problem that aims to obtain clean images from in-the-wild noisy input. Recently, Vision Transformer (ViT) exhibits a strong ability to capture long-range dependencies and many researchers attempt to apply ViT to image denoising tasks. However, real-world image is an isolated frame that makes the ViT build the long-range dependencies on the internal patches, which divides images into patches and disarranges the noise pattern and gradient continuity. In this article, we propose to resolve this issue by using a continuous Wavelet Sliding-Transformer that builds frequency correspondence under real-world scenes, called DnSwin. Specifically, we first extract the bottom features from noisy input images by using a CNN encoder. The key to DnSwin is to separate high-frequency and low-frequency information from the features and build frequency dependencies. To this end, we propose Wavelet Sliding-Window Transformer that utilizes discrete wavelet transform, self-attention and inverse discrete wavelet transform to extract deep features. Finally, we reconstruct the deep features into denoised images using a CNN decoder. Both quantitative and qualitative evaluations on real-world denoising benchmarks demonstrate that the proposed DnSwin performs favorably against the state-of-the-art methods.

Related papers

• Learning Multi-scale Spatial-frequency Features for Image Denoising [58.883244886588336]
  We propose a novel multi-scale adaptive dual-domain network (MADNet) for image denoising.<n>We use image pyramid inputs to restore noise-free results from low-resolution images.<n>In order to realize the interaction of high-frequency and low-frequency information, we design an adaptive spatial-frequency learning unit.
  arXiv  Detail & Related papers  (2025-06-19T13:28:09Z)
• Freqformer: Image-Demoiréing Transformer via Efficient Frequency Decomposition [83.40450475728792]
  We present Freqformer, a Transformer-based framework specifically designed for image demoir'eing through targeted frequency separation.<n>Our method performs an effective frequency decomposition that explicitly splits moir'e patterns into high-frequency spatially-localized textures and low-frequency scale-robust color distortions.<n>Experiments on various demoir'eing benchmarks demonstrate that Freqformer achieves state-of-the-art performance with a compact model size.
  arXiv  Detail & Related papers  (2025-05-25T12:23:10Z)
• How I Warped Your Noise: a Temporally-Correlated Noise Prior for Diffusion Models [7.89220773721457]
  We propose a novel method for preserving temporal correlations in a sequence of noise samples. $int$-noise (integral noise) reinterprets individual noise samples as a continuously integrated noise field. $int$-noise can be used for a variety of tasks, such as video restoration, surrogate rendering, and conditional video generation.
  arXiv  Detail & Related papers  (2025-04-03T22:49:56Z)
• Image Denoising Using Green Channel Prior [3.8541941705185114]
  Green channel prior-based image denoising (GCP-ID) method integrates GCP into classic patch-based denoising framework. To enhance the adaptivity of GCP-ID to various image contents, we cast the noise estimation problem into a classification task and train an effective estimator based on convolutional neural networks (CNNs)
  arXiv  Detail & Related papers  (2024-08-12T05:07:12Z)
• Complex Image Generation SwinTransformer Network for Audio Denoising [20.11487887319951]
  This paper converts the audio denoising problem into an image generation task. We first develop a complex image generation SwinTransformer network to capture more information from the complex Fourier domain. We then impose structure similarity and detailed loss functions to generate high-quality images and develop an SDR loss to minimize the difference between denoised and clean audios.
  arXiv  Detail & Related papers  (2023-10-24T18:21:03Z)
• A cross Transformer for image denoising [83.68175077524111]
  We propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising.
  arXiv  Detail & Related papers  (2023-10-16T13:53:19Z)
• Self-Supervised Image Denoising for Real-World Images with Context-aware Transformer [3.767629593484917]
  We propose a novel Denoise Transformer for real-world image denoising. It is mainly constructed with Context-aware Denoise Transformer (CADT) units and Secondary Noise Extractor (SNE) block. Experiments on the real-world SIDD benchmark achieve 50.62/0.990 for PSNR/SSIM, which is competitive with the current state-of-the-art method.
  arXiv  Detail & Related papers  (2023-04-04T08:30:50Z)
• Multi-stage image denoising with the wavelet transform [125.2251438120701]
  Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. We propose a multi-stage image denoising CNN with the wavelet transform (MWDCNN) via three stages, i.e., a dynamic convolutional block (DCB), two cascaded wavelet transform and enhancement blocks (WEBs) and residual block (RB)
  arXiv  Detail & Related papers  (2022-09-26T03:28:23Z)
• Wavelet Integrated CNNs for Noise-Robust Image Classification [51.18193090255933]
  We enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT) WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.
  arXiv  Detail & Related papers  (2020-05-07T09:10:41Z)
• Reconstructing the Noise Manifold for Image Denoising [56.562855317536396]
  We introduce the idea of a cGAN which explicitly leverages structure in the image noise space. By learning directly a low dimensional manifold of the image noise, the generator promotes the removal from the noisy image only that information which spans this manifold. Based on our experiments, our model substantially outperforms existing state-of-the-art architectures.
  arXiv  Detail & Related papers  (2020-02-11T00:31:31Z)
• Variational Denoising Network: Toward Blind Noise Modeling and Removal [59.36166491196973]
  Blind image denoising is an important yet very challenging problem in computer vision. We propose a new variational inference method, which integrates both noise estimation and image denoising.
  arXiv  Detail & Related papers  (2019-08-29T15:54:06Z)

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.