PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising

• URL: http://arxiv.org/abs/2310.10088v1
• Date: Mon, 16 Oct 2023 05:42:49 GMT
• Title: PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising
• Authors: Hyemi Jang, Junsung Park, Dahuin Jung, Jaihyun Lew, Ho Bae, Sungroh Yoon
• Abstract summary: We propose PUCA, a novel J-invariant U-Net architecture, for self-supervised denoising. PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising.
• Score: 35.641029222760025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although supervised image denoising networks have shown remarkable performance on synthesized noisy images, they often fail in practice due to the difference between real and synthesized noise. Since clean-noisy image pairs from the real world are extremely costly to gather, self-supervised learning, which utilizes noisy input itself as a target, has been studied. To prevent a self-supervised denoising model from learning identical mapping, each output pixel should not be influenced by its corresponding input pixel; This requirement is known as J-invariance. Blind-spot networks (BSNs) have been a prevalent choice to ensure J-invariance in self-supervised image denoising. However, constructing variations of BSNs by injecting additional operations such as downsampling can expose blinded information, thereby violating J-invariance. Consequently, convolutions designed specifically for BSNs have been allowed only, limiting architectural flexibility. To overcome this limitation, we propose PUCA, a novel J-invariant U-Net architecture, for self-supervised denoising. PUCA leverages patch-unshuffle/shuffle to dramatically expand receptive fields while maintaining J-invariance and dilated attention blocks (DABs) for global context incorporation. Experimental results demonstrate that PUCA achieves state-of-the-art performance, outperforming existing methods in self-supervised image denoising.

Related papers

• Self-Calibrated Variance-Stabilizing Transformations for Real-World Image Denoising [19.08732222562782]
  Supervised deep learning has become the method of choice for image denoising. We show that, contrary to popular belief, denoising networks specialized in the removal of Gaussian noise can be efficiently leveraged in favor of real-world image denoising.
  arXiv  Detail & Related papers  (2024-07-24T16:23:46Z)
• Random Sub-Samples Generation for Self-Supervised Real Image Denoising [9.459398471988724]
  We propose a novel self-supervised real image denoising framework named Sampling Difference As Perturbation (SDAP) We find that adding an appropriate perturbation to the training images can effectively improve the performance of BSN. The results show that it significantly outperforms other state-of-the-art self-supervised denoising methods on real-world datasets.
  arXiv  Detail & Related papers  (2023-07-31T16:39:35Z)
• Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network [12.478287906337194]
  Most representative self-supervised denoisers are based on blind-spot networks. A standard blind-spot network fails to reduce real camera noise due to the pixel-wise correlation of noise. We propose a novel self-supervised training framework that can remove real noise.
  arXiv  Detail & Related papers  (2023-04-19T08:55:27Z)
• I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images [8.763680382529412]
  pixel-shuffle downsampling (PD) has been proposed to eliminate the spatial correlation of noise. We propose self-residual learning without the PD process to maintain texture information. The results of extensive experiments show that the proposed method outperforms state-of-the-art self-supervised blind denoising approaches.
  arXiv  Detail & Related papers  (2023-02-21T08:51:17Z)
• Robust Deep Ensemble Method for Real-world Image Denoising [62.099271330458066]
  We propose a simple yet effective Bayesian deep ensemble (BDE) method for real-world image denoising. Our BDE achieves +0.28dB PSNR gain over the state-of-the-art denoising method. Our BDE can be extended to other image restoration tasks, and achieves +0.30dB, +0.18dB and +0.12dB PSNR gains on benchmark datasets.
  arXiv  Detail & Related papers  (2022-06-08T06:19:30Z)
• CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from Image [53.76319163746699]
  We propose a novel and powerful self-supervised denoising method called CVF-SID. CVF-SID can disentangle a clean image and noise maps from the input by leveraging various self-supervised loss terms. It achieves state-of-the-art self-supervised image denoising performance and is comparable to other existing approaches.
  arXiv  Detail & Related papers  (2022-03-24T11:59:28Z)
• AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network [60.650035708621786]
  Blind-spot network (BSN) and its variants have made significant advances in self-supervised denoising. It is challenging to deal with spatially correlated real-world noise using self-supervised BSN. Recently, pixel-shuffle downsampling (PD) has been proposed to remove the spatial correlation of real-world noise. We propose an Asymmetric PD (AP) to address this issue, which introduces different PD stride factors for training and inference.
  arXiv  Detail & Related papers  (2022-03-22T15:04:37Z)
• Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising [54.730707387866076]
  We introduce Noise2Same, a novel self-supervised denoising framework. In particular, Noise2Same requires neither J-invariance nor extra information about the noise model. Our results show that our Noise2Same remarkably outperforms previous self-supervised denoising methods.
  arXiv  Detail & Related papers  (2020-10-22T18:12:26Z)
• Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders [81.30960319178725]
  We propose DivNoising, a denoising approach based on fully convolutional variational autoencoders (VAEs) First we introduce a principled way of formulating the unsupervised denoising problem within the VAE framework by explicitly incorporating imaging noise models into the decoder. We show that such a noise model can either be measured, bootstrapped from noisy data, or co-learned during training.
  arXiv  Detail & Related papers  (2020-06-10T21:28:13Z)
• 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.